social problem solving scale

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Social Problem-Solving Inventory-Revised (SPSI-R)

Thomas J. D’Zurilla, Ph.D., Arthur M. Nezu, Ph.D., & Albert Maydeu- Olivares, Ph.D.

Ages: 13 and older Administration: Self report Administration Time: 15–20 minutes (10 minutes for the Short version) Qualification Level B

Key Areas Measured: Positive Problem Orientation Negative Problem Orientation Rational Problem Solving Problem Definition and Formulation Generation of Alternative Solutions Decision Making Solution Implementation and Verification Impulsivity/Carelessness Style Avoidance Style

Social problem-solving ability has implications for all areas of life, including interpersonal and work-related relationships. The SPSI–R inventory helps determine an individual’s problem solving strengths and weaknesses so that deficits can be addressed and treatment progress can be tracked. This instrument is suitable for educational, healthcare, corrections, or business environments with people who want to explore and develop their social problem-solving abilities. The short version (SPSI–R:S) produces scores for the same five scales as the long version (SPSI–R:L), but does not include the Rational Problem Solving subscales. It is ideal when time is limited or when clients are completing a large test battery.

The SPSI–R normative sample included adolescents, young adults, middle-aged adults, and elderly adults with a total of 1,928 participants. Separate norms are included for each age group. Raw scores are plotted on Profile Sheets for conversion to standard scores.

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Social Problem Solving Inventory Revised (SPSI-R)

The Social Problem Solving Inventory-Revised (SPSI-R) is published and sold by MHS Assesments (it is also distributed by Pearson). It contains 5 scales to measure different dimensions of social problem solving: Positive Problem Orientation, Negative Problem Orientation, Rational Problem Solving, Impulsivity/Carelessness Style, and Avoidance Style. It is an individual assessment with Likert-style responses appropriate for ages 13-18.

Positive Problem Orientation, Negative Problem orientation, Rational Problem Solving, (Problem Definition and Formulation, Generation of Alternative Solutions, Decision Making, Solution Implementation and Verification), Impulsivity/ Carelessness Style, Avoidance Style

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MHS Assessments User Level B required for purchase

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$198.00 for complete pack of 25 long + 25 short forms and technical manual,

$67.00 for 25 long forms or 25 short forms,

$90.00 for technical manual

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Clarke, A. Y., Cullen, A. E., Walwyn, R., & Fahy, T. (2010). A quasi-experimental pilot study of the Reasoning and Rehabilitation programme with mentally disordered offenders. The Journal of Forensic Psychiatry & Psychology , 21 (4), 490-500. https://doi.org/10.1080/14789940903236391

Fowler, N. R., Hansen, A. S., Barnato, A. E., & Garand, L. (2013). Association between anticipatory grief and problem solving among family caregivers of persons with cognitive impairment. Journal of Aging and Health , 25 (3), 493-509. https://doi.org/10.1177/0898264313477133

McGee, C. L., Fryer, S. L., Bjorkquist, O. A., Mattson, S. N., & Riley, E. P. (2008). Deficits in social problem solving in adolescents with prenatal exposure to alcohol. The American Journal of Drug and Alcohol Abuse , 34 (4), 423-431. https://doi.org/10.1080/00952990802122630

Sahler, O. J. Z., Dolgin, M. J., Phipps, S., Fairclough, D. L., Askins, M. A., Katz, E. R., ... & Butler, R. W. (2013). Specificity of problem-solving skills training in mothers of children newly diagnosed with cancer: Results of a multisite randomized clinical trial. Journal of Clinical Oncology, 31 (10), 1329. https://doi.org/10.1200/jco.2011.39.1870

Willems, R. A., Bolman, C. A., Mesters, I., Kanera, I. M., Beaulen, A. A., & Lechner, L. (2016). Cancer survivors in the first year after treatment: the prevalence and correlates of unmet needs in different domains.  Psycho‐oncology ,  25 (1), 51-57. https://doi.org/10.1002/pon.3870

Psychometrics

D’Zurilla, T. J., Nezu, A. M., & Maydeu-Olivares, A. (2002). Social problem-solving inventory-revised.  https://psycnet.apa.org/doi/10.1037/t05068-000  

Hawkins, D., Sofronoff, K., & Sheffield, J. (2008). Psychometric properties of the Social Problem Solving Inventory-Revised Short-Form: Is the short form a valid and reliable measure for young adults? Cognitive Therapy and Research, 33(5), 462–470. https://doi.org/10.1007/s10608-008-9209-7

Maydeu-Olivares, A., Rodrı́guez-Fornells, A., Gómez-Benito, J., & D'Zurilla, T. J. (2000). Psychometric properties of the Spanish adaptation of the Social Problem-Solving Inventory-Revised (SPSI-R). Personality and Individual Differences , 29(4), 699-708. https://doi.org/10.1016/s0191-8869(99)00226-3

Psychometric Considerations

Psychometrics is the science of psychological assessment. A primary goal of EdInstruments is to provide information on crucial psychometric topics including Validity and Reliability – essential concepts of evaluation, which indicate how well an instrument measures a construct - as well as additional properties that are worthy of consideration when selecting an instrument of measurement.

 Learn more

Social Problem Solving Scale

• The interviewer assigns the code for a response category to each verbal response for each picture. The categories and codes are: Aggressive (0), Competent (1), Authority-Punish (2), Authority-Intervene (3), Passive/Inept (4), Irrelevant/Other (5). If a response fits more than one category, it is assigned to the category with the lowest code number.
• The total number of valid responses for each picture is calculated. A valid response is a response that can be assigned to a category. Inept or irrelevant responses are valid. Repetitive responses are an example of invalid responses.
• The percent of responses in each category for each picture is calculated by dividing the number of responses in each category by the total number of valid responses. This calculation yields Picture response percentages.
• The mean of each response category percentage over the eight pictures is calculated to yield Mean-percentages across pictures.
• Year 01 | K | age 6
• Year 02 | grade 1 | age 7
• Year 03 | grade 2 | age 8
• Year 04 | grade 3 | age 9

Raw Dataset Name: CyB

Scored Dataset Name: SPSySCc

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Social problem solving: Theory and assessment

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Psychometric Properties of the Social Problem Solving Inventory-Revised Short-Form: Is the Short Form a Valid and Reliable Measure for Young Adults?

• Original Article
• Published: 20 August 2008
• Volume 33 , pages 462–470, ( 2009 )

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• Deanne Hawkins 1 ,
• Kate Sofronoff 1 &
• Jeanie Sheffield 1  

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The purpose of the present study was to examine the psychometric properties of the Social Problem-Solving Inventory-Revised Short-Form (SPSI-R:SF), a 25-item self-report measure of real life social problem-solving ability. A sample of 219 Australian university students aged 16–25 years participated in the study. The reliability of the SPSI-R:SF scales was adequate to excellent. Evidence was demonstrated for convergent validity and divergent validity. Confirmatory factor analysis results were in line with past research and suggested good model fit. In addition, discriminant function analysis revealed that the SPSI-R:SF was able to significantly discriminate low and high levels of depressive symptomatology. Collectively, results suggest that the SPSI-R:SF represents a reliable and valid instrument for efficient assessment of social problem-solving ability in young Australian adults. Limitations and future research are also discussed.

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Hawkins, D., Sofronoff, K. & Sheffield, J. Psychometric Properties of the Social Problem Solving Inventory-Revised Short-Form: Is the Short Form a Valid and Reliable Measure for Young Adults?. Cogn Ther Res 33 , 462–470 (2009). https://doi.org/10.1007/s10608-008-9209-7

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DOI : https://doi.org/10.1007/s10608-008-9209-7

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A network analysis of social problem-solving and anxiety/depression in adolescents.

• 1 Wenzhou Seventh People's Hospital, Wenzhou, China
• 2 Department of Psychology, School of Education, Wenzhou University, Wenzhou, China

Social problem-solving (SPS) involves the cognitive-behavioral processes through which an individual identifies and copes with everyday problems; it is considered to contribute to anxiety and depression. The Social Problem-Solving Inventory Revised is a popular tool measuring SPS problem orientations and problem-solving styles. Only a negative problem orientation (NPO) is considered strongly related to anxiety and depression. In the present study, we investigated the detailed connections among the five components of SPS and 14 anxiety-depression symptoms and specified the role of NPO and other components in the anxiety-depression network. We employed network analysis, constructed circular and multi-dimensional scaling (MDS) networks, and calculated the network centrality, bridge centrality, and stability of centrality indices. The results were as follows: (1) the MDS network showed a clustering of anxiety and depression symptoms, with NPO and avoidance style components from SPS being close to the anxiety-depression network (demonstrated by large bridge betweenness and bridge closeness); (2) the NPO and positive problem orientation from SPS were most influential on the whole network, though with an opposite effect; (3) strength was the most stable index [correlation stability (CS) coefficient = 0.516] among the centrality indices with case-dropping bootstraps. We also discussed this network from various perspectives and commented on the clinical implications and limitations of this study.

Introduction

Social problem-solving (SPS) is believed to be strongly related to anxiety and depression, which is very popular among Chinese people. For adults, 4% ( 1 ) before and 20.4% ( 2 ) during the COVID-19 epidemic suffer from anxiety and depression; for adolescent, the prevalent of anxiety and depression is 11.2%/14.6% ( 3 ) before and 19%/36.6% ( 4 ) during the epidemic. SPS plays a significant role in psychological adjustment and constitutes an important coping strategy that has the potential to reduce or minimize psychological distress ( 5 , 6 ). Previous research has found that strong SPS abilities reduce the morbidity associated with anxiety and depression by aiding young people in controlling and modifying their health behavior ( 7 ); they are of key importance in managing emotions and wellbeing ( 8 ). Conversely, poor problem orientation has consistently linked depression and anxiety ( 9 ). Furthermore, depressed patients frequently exhibit deficiencies in social problem-solving, producing fewer effective solutions than do normal control subjects ( 10 ).

Essentially, SPS involves the cognitive-behavioral processes through which an individual identifies and copes with everyday problems ( 11 ). It comprises problem orientation (a general motivational and appraisal component) and problem-solving style (the cognitive and behavioral activities a person uses to cope with problems). The Social Problem-Solving Inventory Revised (SPSI-R) provides a corresponding scale and comprehensive assessment of all theoretical components linked to contemporary models of social problem-solving [i.e., both problem orientation and problem-solving style ( 12 , 13 )]. The SPSI-R consists of a scale of 25 (in the short form) or 52 (in the long form) items, and is one of the most prominent instruments used to study SPS ( 14 ). The SPSI-R is a theory-based measure of SPS processes. It consists of five dimensions, as follows: (1) positive problem orientation (PPO), (2) negative problem orientation (NPO), (3) rational PPO problem-solving (RPS), (4) impulsivity/carelessness style (ICS), and (5) avoidance style (AS). The SPSI-R assesses a person's perception of his or her general approach to and styles of solving problems in everyday living that have repeatedly been found to be reliable and valid ( 15 , 16 ).

SPSI-R research has shown that SPS is an important measure of psychological distress, wellbeing, and social competence [i.e., depression, distress, anxiety, health-related behaviors, life satisfaction, optimism, situational coping, aggression, and externalizing behaviors ( 17 – 19 )]. Previous research has found that certain specific components of SPS can contribute significantly to anxiety and depression. For example, anxious and depressed patients may have difficulties at different stages of the problem-solving process ( 20 , 21 ); Kant et al. (author?) ( 22 ) found that all five problem-solving dimensions measured by the SPSI-R were significantly related to both anxiety and depression in at least one of two samples (i.e., the middle aged and elderly); additional follow-up analyses indicated that NPO contributed most to the significant mediating effect between problems and depression.

Specifically, NPO is strongly related to depression and emotional distress. Abu-Ghazal and Falwah ( 23 ) found that employing PPO to solve problems leads to positive psychological wellbeing, while NPO is associated with depression. In Australia, researchers examined the relationship between NPO and depression-anxiety in 285 young adults using the NPO dimensions of the SPSI-R, finding strong connections between the two ( 24 ). Additionally, many researchers have found that social anxiety is related to NPO ( 25 , 26 ). In Hungary, Kasik and Gál ( 27 ) studied the relationships among SPS, anxiety, and empathy in 445 Hungarian adolescents, finding that regardless of age, adolescents with an increased level of anxiety also have high levels of NPO and AS. Furthermore, studies have found a link between NPO and stress ( 28 – 32 ). Therefore, anxiety and depression have the strongest association with NPO, above all other SPS components ( 8 , 33 – 35 ), and success in reducing symptoms of anxiety and depression appears to be more strongly predicated on the absence of NPO than presence of PPO ( 34 ).

These studies suggest that NPO plays an important role in anxiety and depression. We also explored the detailed connections between problem-solving orientations (including NPO) and problem-solving styles with anxiety-depression symptoms. In other words, we integrated the components of SPS into the anxiety-depression network and investigated the link between these components and anxiety-depression symptoms. We identified the components of social problem-solving most strongly associated with certain symptoms in the anxiety-depression network and determined which components were most centrally located.

Thus, network analysis was employed to analyze the relationships among components of SPS and anxiety-depression symptoms, working from the bottom up, without applying any top-down construct consistent with the standard biomedical and reductionist model ( 36 ). A key premise of network theory is that psychopathological symptoms are interacting and reinforcing parts of a network, rather than clusters of underlying disorders ( 37 ). To test this argument, network analysis has been used to describe the relationships within and between disorders ( 37 ). The dynamics and interrelationships between comorbidities can be identified in network analysis and gaps not considered by factor analysis methods can be addressed ( 38 ). A network is defined as a set of nodes (symptoms) and edges (connections between nodes). In a network model, the symptoms themselves constitute the disorder. The onset and maintenance of symptoms are determined by tracing the pathways of the network ( 38 ).

In an estimated network structure, a centrality measure denotes the overall connectivity of a particular symptom (or component). Central nodes contribute the most to the interrelatedness of symptoms (or components) within the estimated network structure ( 39 , 40 ). A tightly connected network with many strong connections among the symptoms is considered risky because activation of one symptom can quickly spread to other symptoms, leading to more chronic symptoms over time ( 41 ). In other words, when a highly central component is activated (i.e., a person reports the presence of a symptom), it influences other components, causing them to become activated as well, and thus maintaining the network. Considering the importance of problem orientation and problem-solving styles to emotional wellbeing, the nodes should be strongly linked to symptoms of anxiety and depression. In addition, we calculated the bridge-centrality. Previous research has found that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another) ( 42 ). Through this network analysis, we gained insight regarding the relationship between SPS and anxiety-depression, which may have clinical implications such as helping to modify patients' problem-solving styles to alleviate related symptoms.

In summary, social problem solving is highly correlated with anxiety and depression and can lead to a number of mental illnesses. There are few study about how the aspects of social problem solving that contribute to depression and anxiety and how they both interact with each other. The present study is to explore the detailed connections between problem-solving orientations and problem-solving styles with anxiety-depression symptoms. NPO, specifically, is hypothesized to be related to depression and emotional distress. We characterized the network structure of SPS components and anxiety-depression symptoms using psychiatric and regular samples. We first investigated the node and bridge centrality, and then determined the stability of the centrality indices for the network.

Participants

The samples, consisting of adolescents aged 12–17 years, was obtained from a psychiatric hospital and two secondary schools, collected from October 2021 and completed in March 2022. The 100 adolescents from the hospital were outpatients who had mental health assessments done by psychiatrists. When patients enter the psychological assessment room, they are briefly introduced to the purpose of our study and then asked to fill out the relevant scales based on the most recent week. They could ask the psychiatrists for help if they have any questions. When the task was finished, the psychiatrists have a check to make sure that all responses are completed, and then the subject leaves the assessment room. The other 100 participants were randomly selected middle school students; they conducted the self-rating assessments while monitored by their teachers in the classrooms. All participants signed an informed consent form and were explained about the rules regarding anonymity, confidentiality, and their right to quit.

Ten samples (from the middle schools) were excluded from data collection because they failed the manipulation check ( 43 ). Therefore, 190 participants were included in the data analysis.

Hospital anxiety and depression scale

The HADS assesses both anxiety and depression, which commonly coexist ( 44 ). The measure is employed frequently, due to its simplicity, speed, and ease of use. Very few literate people have difficulty completing it. The HADS contains a total of 14 items, including seven for depressive symptoms (i.e., the HADS-D) and seven for anxiety symptoms (i.e., the HADS-A), focusing on symptoms that are non-physical. The correlations between the two subscales vary from 0.40 to 0.74 (with a mean of 0.56). The Cronbach's alpha for the HADS-A varies from 0.68 to 0.93 (with a mean of 0.83) and for the HADS-D from 0.67 to 0.90 (with a mean of 0.82). In most studies, an optimal balance between sensitivity and specificity was achieved when a cut point was set at a score of 8 or above on both the HADS-A and HADS-D. The sensitivity and specificity for both is 0.80. Many studies conducted around the world have confirmed that the measure is valid when used in a community setting or primary care medical practice ( 45 ).

SPSI-R (Chinese version)

There have been several revised versions of the SPSI-R for use in the Chinese language, such as the measure published by Siu and Shek ( 46 ) and Wang ( 47 ). The present study used the latter, which shows both good reliability and validity. The overall Cronbach's alpha is 0.85, and the RPS, AS, NPO, PPO, and ICS subscales are 0.85, 0.82, 0.70, 0.66, and 0.69, respectively. The SPSI-R uses a five-point Likert-type scale ranging from 0 to 4, as follows: (0) Not at all true for me, (1) slightly true for me, (2) moderately true for me, (3) very true for me, and (4) extremely true for me.

Network analysis

We used a Gaussian graphical model (GGM) to build the network via the R package (R Core Team version 4.1.3) qgraph (version 1.9.2) ( 48 , 49 ). GGMs estimate many parameters (i.e., 19 nodes required the estimation of 171 parameters: 19 threshold parameters and 19 * 18/2 = 171 pairwise association parameters) that would likely result in false positive edges. Therefore, it is common to regularize GGMs via a graphical lasso ( 49 – 51 ), leading to a sparse (i.e., parsimonious) network that explains the correlation or covariance among nodes with as few edges as necessary. Node placement was determined by the Fruchterman-Reingold (FR) algorithm, which places nodes with stronger average associations closer to the center of the graph ( 52 ). The R package qgraph was used to calculate and visualize the networks. We also measured the centrality and stability of the established network. The R package qgraph and estimatenetwork automatically implement the glasso regularization, in combination with an extended Bayesian information criterion (EBIC) model, as described by Foygel and Drton ( 53 ).

In network parlance, anxiety-depression symptoms and SPS components are “nodes” and the relationships between the nodes are “edges”. The edge between two nodes represents the regularized partial correlation coefficients, and the thickness of the edge indicates the magnitude of the association. The graphical lasso algorithm makes all edges with small partial correlations shrink to zero, and thus facilitates interpretation and establishment of a stable network, solving traditional lost-power issues that emerge from examining all partial correlations for statistical significance [for greater detail, see ( 54 )]. For the present network, we divided the components into three groups or communities: anxiety (seven symptoms), depression (seven symptoms), and SPS (five components).

Most network studies in psychopathology have used the FR algorithm to plot graphs ( 52 ). The FR algorithm is a force-directed graph method [see also ( 55 )] that is similar to creating a physical system of balls connected by elastic strings. Importantly, the purpose of plotting with a force-directed algorithm is not to place the nodes in meaningful positions in space, but rather to position them in a manner that allows for easy viewing of the network edges and clustering structures ( 56 ). We used the “circle” layout for easier viewing, which places all nodes in a single circle, with each group (or community) put in separate circles (see Figure 1A ). In addition, we employed a multi-dimensional scaling (MDS) approach to display the network (see Figure 1B ). MDS represents proximities among variables as distances between points in a low-dimensional space [e.g., two or three dimensions; ( 57 )]. MDS is particularly useful for understanding networks because the distances between plotted nodes are interpretable as Euclidean distances ( 56 ).

Figure 1 . Estimated network structure based on a sample of 190 adolescents. The network structure is a GGM, which is a network of partial correlation coefficients. Green edges represent positive correlations and red edges indicate negative correlations. The thickness of the edge reflects the magnitude of the correlation. (A) Network structure with the “circle” layout for easy viewing, but it is important to note that the node positions don't indicate Euclidean distances. (B) Network structure with MDS, showing proximities among variables as distances between points in a low-dimensional space.

We calculated several indices of node centrality to identify the symptoms or components most central to the network ( 58 ). For each node, we calculated the strength (i.e., the absolute sum of edge weights connected to a node), closeness (i.e., the average distance from the node to all other nodes in the network), betweenness (i.e., the number of times a node lies on the shortest path between two other nodes), and expected influence (i.e., the sum of edge weights connected to a node). For SPS and anxiety-depression networks considering the relationship in both direction (i.e., both positive and negative), strength rather than expected influence (which only calculates neutralized influence) is suitable. The node bridge strength is defined as the sum of the value of all edges connecting a given node in one community with nodes in other communities, and was computed by the R-package networktools ( 42 ). Higher node bridge strength values indicated a greater increase in the risk of contagion to other groups or communities ( 42 ).

Stability of centrality indices

We investigated the stability of centrality indices by estimating network models based on subsets of the data and case-dropping bootstraps ( n = 1,000). If correlation values declined substantially as participants were removed, we considered this centrality metric to be unstable. The robustness of the network was evaluated by the R-package bootnet using the bootstrap approach ( 54 ). This stability was quantified using the CS coefficient, which quantified the maximum proportion of cases with a 95% certainty that could be dropped to retain a correlation with an original centrality higher than 0.7 (by default) ( 54 ).

The students' average age was 15.54 years ( SD = 1.302); the group included 102 males and 88 females. We conducted descriptive statistics for the scores of each scale on different demographic variables. The results are shown in Table 1 , which demonstrate the number of participants in each group and the mean score and standard deviation (in the parenthesis) for each scale. Due to some missing data for some participants, the total the number of people with different conditions does not equal 190.

Table 1 . The descriptive statistics of the six SPS components, anxiety, and depression.

As for the network, ~41.5% of all 171 network edges were set to zero by the EBICglasso algorithms. Figure 1 presents the network of SPS components and anxiety-depression symptoms. Figure 1A displays an easily viewable circular network with weights on each edge. For example, the strongest edge (weight = 0.32) among the anxiety symptoms was between Btt 1 (“I get sort of a frightened feeling, like 'butterflies' in the stomach”) and Pnc (“I get sudden feelings of panic”). Among depression symptoms, the strongest edge (weight = 0.25) was between Chr (“I feel cheerful”) and Fnn (“I can laugh and see the funny side of things”). For SPS components, the strongest edge (weight = 0.46) was between PPO (positive problem orientation) and RPS (rational problem-solving). Figure 1B display the MDS network. Highly-related nodes appear close together, whereas weakly-related nodes appear further apart. The anxiety-depression symptoms and SPS components cluster within their own communities, and anxiety-depression nodes are closer to each other. The NPO (negative problem orientation) and AS (avoidance style) nodes are nearest to the anxiety-depression network, while other components are distant from that network.

Centrality indices

For the centrality indices, the values were scaled (i.e., normalized) relative to the largest value for each measure. Figure 2 shows the centrality indices, which are ordered by strength . For strength , Rlx (“I can sit at ease and be relaxed”) from the anxiety symptoms is the most central symptom, 2 followed by Frw (“I look forward with enjoyment to things”) from the depression symptoms and PPO (positive problem orientation) from the SPS components, indicating that these nodes had the strongest relationships to the other nodes. For closeness and betweenness , Frw again ranked the highest, indicating that it was closest to all other nodes in the network and on the shortest path between two other nodes. As for expected influence , considering the direction of the relationship (both positive and negative), Rlx and Pnc from the anxiety community was most positively and PPO most negatively influential on the whole network, indicating that Rlx may be an important risk factor and PPO an important protective factor. NPO most positively influenced the network from the SPS community, and Slw (“I feel as if I am slowed down”) did the same for the depression community.

Figure 2 . Centrality indices for the nodes of the present network including those for strength betweenness closeness expected influence. The values are normalized to be within the range of 0–1. The full names of the abbreviations can be found in Figure 1 .

We also calculated the bridge centrality indices (see Figure 3 ). Rlx, Frw , and NPO for anxiety-depression and SPS were found to have the strongest connections (i.e., bridge strength) with other communities ( 42 ). For bridge closeness, Frw, AS , and NPO ranked the highest. For bridge betweenness, Frw, AS , and ICS comprised the top three. For bridge expected influence, Rlx, Slw , and NPO were the most influential.

Figure 3 . Estimated bridge centrality indices for the present network, including bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. The full names of the abbreviations for the nodes can be found in Figure 1 .

Stability of the centrality indices

Figure 4 shows that the average correlations dropped between the centrality indices of networks sampled with persons and the original sample. The stability levels of closeness and betweenness dropped steeply, while the stability levels of the node strength and expected influence less so. The Correlation-Stability (CS) coefficient value should preferably be above 0.5 and not be below 0.25 ( 59 ). In this research, the CS coefficient indicated that the betweenness [CS (cor = 0.7) = 0.205] was not stable, while the closeness [CS (cor = 0.7) = 0.437] was relatively stable in the subset cases. Node strength and expected influence performed best [CS (cor = 0.7) = 0.516], reaching the cutoff of 0.5 and indicating that the metric was stable. Therefore, we found that the order of node strength and expected influence were most interpretable (with some care), while the order of betweenness was not.

Figure 4 . Average correlations between the centrality indices of networks sampled with persons and the original sample. Lines indicate the means and areas ranging from the 2.5th quantile to the 97.5th quantile.

Anchored in the network perspective ( 39 ), this study illustrated the node pathways, central indices, and central bridging indices for the SPS and anxiety-depression networks. From a “network-network” perspective, the node connections were closer within (vs. between) the anxiety-depression and SPS networks, demonstrating their relative independence from one other. This result is in keeping with previous comorbidity studies of anxiety and depression that employed network analysis ( 60 , 61 ), underscoring that the SPS network is distant from the anxiety-depression network (though the NPO and AS nodes are close to the anxiety-depression network, which can be measured by bridge closeness, as seen in Figure 3 ). Further, the SPS seems more strongly related to anxiety than depression networks, given the longer mean distance from SPS to depression. The reason could be that anxiety is more related to problems or events (the uncertainty of the future) ( 62 ) while depression is more related to self (usually accompanied by low self-esteem, low self-efficacy, and hopelessness) ( 63 ). This explanation is reasonable but required further verifications. The MDS structure is a useful tool for displaying the spatial relationships of nodes, and thus its use should be encouraged in the future.

From a “nodes-in-network” perspective, the node centrality indices revealed that the NPO node from SPS and Rlx and Frw from anxiety-depression were likely to be the most central in the entire SPS-anxiety-depression network. Considering that mood disorders affect how people look at and deal with problems, it is appropriate to put anxiety, depression, and SPS components into a single network. In terms of clinical implications, from our results, we can infer that therapy will yield the greatest rewards by modifying NPO , encouraging relaxation training, and enhancing the expectation of enjoyment for coming things. In addition, the NPO and AS nodes are nearest to the anxiety-depression network, especial to the anxiety symptoms. Therefore, we may even consider that NPO and AS (very close to each other) are innate components of anxiety, as anxious people are worried about the future but do not positively view the problem and do not actively cope with the problem ( 64 ). However, this hypothesis requires further confirmation.

From a “network-node-network” perspective, the results of bridge centrality found that the NPO in SPS community had the strongest association (for both bridge strength and bridge closeness) with the anxiety-depression network, echoing previous research that NPO most strongly contributes to anxiety and depression. However, PPO is located away from the anxiety-depression network and the most negatively correlated ( 65 ), as can be seen from the low levels of bridge expected influence and bridge closeness. Furthermore, the RPS node is strongly connected with PPO but valued low in the four indices of bridge centrality, indicating its unimportance because both of them should “stay away” from the network which is main consists of negative nodes ( 66 ). In short, PPO is the protective and NPO the risk factor for the anxiety-depression network. In clinical settings, encouraging PPO and discouraging NPO would be an effective approach to reducing symptoms of anxiety and depression.

Some limitations of this research will direct future research. First, a cross-sectional design was adopted to build the SPS and anxiety-depression networks. Therefore, this study could not be used to ascertain whether anxiety-depression symptoms impact SPS components or vice versa. Thus, future work will adopt a longitudinal approach with repeated measures of anxiety-depression and SPS components to clarify the causal relationship between anxiety-depression and SPS components. Second, it is probable that the detected potential pathways among the components are limited to the SPSI-R and HADS scales applied. Self-report tools for the SPSI-R and anxiety-depression usually vary in their constructs. This diversity limits the connections that can be found in terms of network structure. Nevertheless, the scales we used are broadly employed; they were carefully implemented based on their psychometric constructs and applicability for adolescents. Therefore, the present research adds to the literature of how among adolescents, anxiety-depression symptoms may be associated with SPS components. This study may also act as an incentive for future research applying other scales for SPS and anxiety-depression to ascertain the stability of these novel findings.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by Ethics Committee of Wenzhou Seventh People's Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author contributions

Q-NR conceived and designed the experiments. W-JY and CC performed the experiments. ZL, Q-NR, and W-JY wrote and revised the manuscript. ZL gave financial support. All authors contributed to the article and approved the submitted version.

This research was supported by the Medicine and Health Science and Technology Project of Zhejiang, China (No. 2019KY669), and Wenzhou Science and Technology Project of Zhejiang, China (Y20210112).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ Following, the node labels with abbreviations will be in italics.

2. ^ Rlx (“I can sit at ease and be relaxed”) and Frw (“I look forward with enjoyment to things”) are not symptoms per se , but for measuring the symptoms “restless” and “pessimistic” using reverse questions.

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Keywords: network analysis, social problem-solving, anxiety, depression, adolescent

Citation: Ruan Q-N, Chen C, Jiang D-G, Yan W-J and Lin Z (2022) A network analysis of social problem-solving and anxiety/depression in adolescents. Front. Psychiatry 13:921781. doi: 10.3389/fpsyt.2022.921781

Received: 16 April 2022; Accepted: 21 July 2022; Published: 10 August 2022.

Reviewed by:

Copyright © 2022 Ruan, Chen, Jiang, Yan and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wen-Jing Yan, eagan-ywj@foxmail.com ; Zhang Lin, 409814552@qq.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Social Problem-Solving Inventory for Adolescents (SPSI-A): development and preliminary psychometric evaluation

Affiliation.

• 1 Department of Health, Physical Education, and Recreation Western Michigan University, USA.
• PMID: 7760259
• DOI: 10.1207/s15327752jpa6403_10

This article describes a multiphase developmental process and psychometric evaluation of the Social Problem-Solving Inventory for Adolescents (SPSI-A). The SPSI-A consists of the following three scales: Automatic Process, Problem Orientation, and Problem-Solving Skills. The three subscales of the Problem Orientation Scale include Cognition, Emotion, and Behavior. The four subscales of the Problem-Solving Skills scale consist of Problem Identification, Alternative Generation, Consequence Prediction, and Implementation/Evaluation/Reorganization. Preliminary internal consistency, stability, content, construct, and criterion validity data are presented for freshmen and sophomore high school students. Collectively, the data provide evidence that the SPSI-A is a promising measure of adolescent problem-solving skills and motivation.

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Social Problem Solving and Health

Counseling psychology is committed to helping people meet the challenges and solve the problems they encounter in daily routines and in stressful circumstances. To a great extent, this holds true for other professional psychology specialties (including clinical, educational and health psychology) as clients usually seek professional assistance in solving the problems they face. Thus, the study of problem-solving abilities—their measurement and correlates—and efficient ways to improve these abilities is of keen interest to clinicians and researchers.

Counseling psychology has played an influential role in this area of inquiry. Historically guided by early cognitive-behavioral theorists (D’Zurilla & Goldfried, 1971), counseling psychology contributed essential theoretical refinements ( Heppner & Krauskopf, 1987 ) and measurement tools ( Heppner, 1988 ) that remain landmark events. However, related and subsequent theoretical and empirical contributions—appearing primarily in outlets associated with clinical and health psychology, and in the larger, multidisciplinary literature—have yet to be sufficiently integrated with contributions from counseling psychology. This lack of scholarly integration has not necessarily impeded advancements and applications, but it has thwarted a deeper theoretical understanding of the mechanisms at work in the learning and application of social problem-solving abilities.

Historical Backdrop

The historical backdrop of theory and research must be considered for us to appreciate the subsequent developments in the current literature. The D’Zurilla and Goldfried (1971) is the intellectual wellspring for this area: In this paper, the authors described the elements that would eventually characterize the problem-solving process. Specifically, it was argued that successful problem-solving consists of identifying a problem, defining the characteristics and important aspects of the problem, generating possible solutions and alternatives for the problem, choosing a viable solution and implementing it, and then monitoring and evaluating the progress of the solution.

Two important features of this paper should be emphasized. First, as Nezu (2004) observes, the proposed model of this work was prescriptive rather than descriptive ; that is, D’Zurilla and Goldfried construed effective problem solving principles as they should be and as they should operate, in theoretical terms. Second, the authors did not recommend a specific measure for assessing problem solving skills; their essay was primarily concerned with the ramifications of their straightforward model for cognitive-behavioral interventions.

The implications of this model for counseling psychology were spelled out in an important conceptual review by Heppner (1978) and demonstrated in an impressive intervention study by Richards and Perri (1978) . These papers—both published in the same volume of Journal of Counseling Psychology —exemplified the two different approaches to the study of problem-solving abilities that persist to this day. In the former, Heppner considered the larger cognitive-behavioral framework in which problem solving was a part, drawing out implications for counseling practice and research. Eventually, Heppner’s work produced the Problem Solving Inventory (PSI; Heppner, 1988 ), accompanied by an impressive program of empirical research that demonstrated the correlates and properties of the PSI (for reviews of this work see Heppner & Baker, 1997 ; Heppner, Witty, & Dixon, 2004 ). In contrast, Richards and Perri took initiative from the prescription of problem-solving abilities stipulated by D’Zurilla and Goldfried, developed an intervention based on these principles, and provided evidence of their utility in significantly improving self-management skills of undergraduates ( Richards & Perri, 1978 ).

In surveying the current landscape, we find relevant research that extends from the Heppner research program. This influence is rather easy to identify, as most of this work relies on the PSI (perhaps the most frequently used problem solving measure). This work appears predominately in the counseling psychology literature. The most comprehensive theoretical commentary on this scholarship appears in Heppner and Krauskopf (1987) , in which an information-processing model is used to help us understand how individuals learn, regulate, and execute problem-solving abilities.

Running parallel to this stream of work (with a few intriguing moments of empirical overlap) are studies that integrate the problem-solving principles into interventions with considerable success. Although D’Zurilla and colleagues were apparently uninterested in developing a measure of problem solving abilities at first—indeed, some of the initial intervention studies used Heppner’s PSI ( Nezu & Perri, 1989 )— this camp provided theoretical refinements of the cognitive-behavioral mechanisms of the problem-solving process ( D’Zurilla & Nezu, 2007 ). A measure of social problem-solving abilities was eventually developed (featuring 70 items; D’Zurilla & Nezu, 1990 ) and empirically refined (52 items; D’Zurilla, Nezu, & Maydeu-Olivares, 2002 ). However, this research stream is best characterized by the number of intervention studies that appeared in journals associated with clinical and counseling psychology, and the far-reaching implications of this work are now being realized by multidisciplinary research teams across the health professions.

Theoretical Distinctions

Although these two streams of work often compliment the other, a few compelling theoretical distinctions should be noted. In the Heppner and Krauskopf (1987) model, for example, problem solving is construed as a metacognitive variable that has organizational properties. In a manner akin to Bandura’s self-efficacy model ( Bandura, 1986 ), problem solving is a self-appraisal process, as behavior is influenced by subjective beliefs and perceptions of abilities, competencies, and potential. These cognitions regulate emotional experiences and expression, overt behavior, personal goals and goal-directed activity. The PSI features three empirically derived factors (Personal Control, Problem-Solving Confidence, and Approach-Avoidance), but it is not construed as a measure of actual problem-solving abilities, per se. The favored terminology emphasizes the phenomenological processes stipulated in this model (e.g., “problem-solving appraisal” and “self-appraised problem-solving abilities”).

The Social Problem-Solving Inventory-Revised (SPSI-R; D’Zurilla & Nezu, 1990 ) was developed as the authors recognized two broad functions of social problem-solving abilities they termed problem orientation and problem-solving skills (see Nezu & D’Zurilla, 1989 ). The problem orientation component, based on converging evidence from research at that time, served to regulate emotions, maintain a positive attitude necessary for solving problems, and motivate a person toward solving problems in routine and stressful circumstances. The problem-solving skills component encompassed the actual skills individuals use in solving problems, including rational skills, avoidance, and impulsive and careless styles. This model guided much of the contemporary research that has used this scale. The theoretical and clinical focus of this group centers on the prescriptive nature of the original model ( D’Zurilla & Nezu, 1999 ; Nezu, 2004 ) and consistently uses the term “social problem-solving abilities.” Recently, D’Zurilla and colleagues recognize the strong associations that have occurred between the positive orientation scale on the SPSI-R measure and the rational-problem solving scale, and between the negative problem orientation scale and the impulsive/careless and the avoidance scales ( D’Zurilla, Nezu, & Maydeu-Olivares, 2004 ). They use the terms “constructive problem-solving style” and “dysfunctional problem-solving style” in their recent conceptualization.

PERSONAL ADJUSTMENT AND HEALTH

We acknowledge that personal adjustment is an important aspect of “health,” generally, and it is a dubious enterprise to separate adjustment into dualistic notions of “mental” and “physical” health. The study of social problem-solving and emotional adjustment has largely dominated the relevant counseling literature, and only recently have we begun to appreciate the theoretical and clinical implications of social problem-solving abilities and physical health. From our perspective, we are fairly confident in the established associations between ineffective problem-solving abilities and depression, anxiety, and distress among people in general ( Heppner, et al., 2004 ; Nezu, 2004 ). However, ineffective problem-solving abilities are inconsistently associated with indicators of health-compromising behaviors (e.g., sedentary behavior, substance abuse; Elliott et al., 2004 ). Social problem-solving abilities can be significantly predictive of important self-reported outcomes (e.g., disability, well-being; Elliott, et al., 2004 ) and with objectively-rated indicators of therapeutic adherence (although the directions of these relationships are not always clear; see Herrick & Elliott, 2001 ).

In the remainder of this chapter, we address recent advancements in our understanding of social problem-solving abilities from recent research in emotional, interpersonal and social adjustment associated with health, with health outcomes and secondary complications, and from problem-solving interventions among persons with various health conditions. We then turn our attention to major issues and findings raised in published reviews of the research to date, and conclude with a discussion of the problems we see in this work and offer our recommendations for future research. We use the term “social problem-solving abilities” in deference to the original model and in light of the currency of this concept in the larger multidisciplinary literature (in which much of the research relevant to our discussion has appeared).

Emotional, Interpersonal, and Social Adjustment

In a previous survey of problem-solving abilities and health, the connections between dysfunctional social problem-solving styles and depression and distress were theoretically consistent across the relevant literature; data linking effective problem-solving abilities with optimal adjustment were decidedly mixed ( Elliott, et al., 2004 ). Empirical research over the ensuing years has yielded similar results. A negative problem orientation has been associated with higher depression scores among older persons with vision loss ( Dreer, Elliott, Fletcher, & Swanson, 2005 ) and among family caregivers of persons with severe disabilities ( Grant et al., 2006 ; Rivera, Elliott et al., 2006 ). A dysfunctional problem-solving style—as measured by the SPSI-R—may be particularly characteristic of individuals who meet diagnostic criteria for a suspected major depressive disorder ( Dreer, Elliott, Shewchuk, Berry, & Rivera, in press ; Grant, Weaver, Elliott, Bartolucci, & Giger, 2004 ; Rivera, Elliott, Berry, Oswald, & Grant, 2007 ).

Indicators of function and quality of life among persons with debilitating conditions rely heavily on self-report measures of these constructs. These measures may be influenced by respondent problem-solving styles, independent of objectively-defined indicators of disability severity ( Elliott, Godshall, Herrick, Witty, & Spruell, 1991 ; Shaw, Feuerstein, Haufler, Berkowitz, & Lopez, 2001 ). Consistent with these data, Rath and colleagues found ineffective problem-solving abilities were significantly associated with self-reported psychosocial impairment among persons with traumatic brain injuries (TBI; Rath, Langenbahn, Simon, Sheer, Fletcher, & Diller, 2004 ). Similar results have been found among persons in a chronic pain rehabilitation program ( Witty, Heppner, Bernard, & Thoreson, 2001 ). A negative problem orientation is a stronger predictor of psychosocial impairment than health locus of control variables ( Shanmugham, Elliott & Palmatier, 2004 ).

In fact, among persons with TBI, there is evidence that social problem-solving abilities may be a better predictor of community integration following medical rehabilitation than several neuropsychological measures often used to predict adjustment in this population ( Rath, Hennessy, & Diller, 2003 ). These results—consistent with prior evidence of the social adaptability associated with effective problem-solving (see Heppner, et al., 1982 , and Neal & Heppner, 1986 )—may prove particularly enlightening in our appreciation of interpersonal and social dynamics of adjustment following disease and disability.

Although the results from these studies have been largely consistent with previous research, the evidence linking social problem-solving abilities and optimal adjustment remains thin. For example, prospective research has found a positive orientation predictive of well-being among family caregivers of stroke survivors over thirteen weeks after discharge from an inpatient rehabilitation program ( Grant et al., 2006 ). Cross-sectional research has found a negative orientation to be inversely associated with caregiver mental health and life satisfaction ( Rivera et al., 2006 ), and Dreer et al. (2005) found elements of constructive and dysfunctional problem-solving styles were associated with the life satisfaction reported by individuals in an outpatient low vision rehabilitation program.

A more detailed analysis of subgroups within a large sample of individuals with disabilities suggests that the relationship of problem-solving abilities to measures of distress and well-being may be theoretically consistent at the extremes: Effective problem-solving abilities are associated with a more optimal profile, and ineffective abilities are associated with opposite clinical picture ( Elliott, Shewchuk, Miller, & Richards, 2001 ). However, two other clusters revealed that some individuals who harbor a negative orientation and who report rational problem-solving skills also experience considerable distress. Our lack of insight into the actual mechanisms by which problem solving influences adjustment in routine, daily experiences hinders our interpretation of these data and their implications.

A similarly complicated pattern emerges in our understanding of self-reported health and social problem-solving abilities. Prospective research has found a negative orientation to be productive of family caregiver health complaints over the course of a year ( Elliott, Shewchuk, & Richards, 2001 ). Yet cross-sectional study with family caregivers of persons with various disabilities did not replicate this finding ( Rivera et al., 2006 ), and Grant et al. (2006) found a significant—albeit tenuous and diminishing—relationship between a positive orientation and general health over 13 weeks. Despite early evidence that a negative orientation is predictive of self-reported health complaints in cross-sectional and prospective designs ( Elliott & Marmarosh, 1994 ), it appears that several unmeasured factors may account for these inconsistent findings.

There is reason to believe that social problem-solving abilities operate within interpersonal and social contexts to exert an influence on adjustment. An effective problem-solving style has been associated with greater relationship satisfaction among family caregivers of stroke survivors ( Shanmugham, et al., 2007 ). Related research suggests that children of families that rely on problem-solving coping fare better over time than families who rely less on these strategies ( Kinsella, Ong, Murtagh, Prior, & Sawyer, 1999 ; Rivara, Jaffe, Polissar, Fay, Liao, & Martin, 1996 ). Furthermore, persons living with severe disability and with family caregivers who have impulsive and careless ways of solving problems were more likely to have a pressure sore within the first year of acquired disability than other individuals ( Elliott, Shewchuk, & Richards, 1999 ). Caregiver dysfunctional styles have also been implicated in the distress and decreased life satisfaction reported by patients with congestive heart failure ( Kurylo, Elliott, DeVivo, & Dreer, 2004 ).

A comprehensive study by Johnson and colleagues (2006) suggests that the effects of problem solving on distress may be defined by several adaptive correlates of social problem-solving abilities. In this study, distress—as a latent construct—was composed of decreased social support, elevations in depression and negative mood, and high stress among 545 HIV+ adults, and distress was predicted by constructive and dysfunctional problem-solving styles (accounting for over 60% of the variance). Although prior research has indicated that social problem-solving abilities are usually related to these separate variables in a theoretically consistent fashion, this was the first study to demonstrate these relationships in a comprehensive model, and the associations were best understood within the context of this model.

Health Outcomes and Secondary Complications

In many respects, social problem-solving abilities have demonstrated considerable utility as a predictor of important health outcomes in several studies of depression among persons living with chronic health conditions. Depression is often conceptualized as an important health outcome because it is associated with increased heath care costs and it compromises the overall health of persons with conditions as varied as diabetes, paralysis, and congestive heart failure.

It has been difficult to ascertain the ways in which problem-solving abilities might influence other, more objectively-defined health outcomes. Data concerning the relations of problem solving to substance use, exercise, and other health behaviors have been mixed (see Elliott et al., 2004 ), although among individuals who live with a disability there is some indication that a dysfunctional style may be associated with health-compromising behaviors ( Dreer, Elliott, & Tucker, 2004 ).

The Johnson et al. (2006) study again informs us of the ways in which problem-solving abilities may influence health outcomes. In this attempt to predict adherence to antiretroviral therapy (assessed by a survey of the number of pills skipped during a 3-day period), the final model revealed no significant, direct paths from the two social problem-solving latent variables (constructive, dysfunctional) to adherence. Rather, social problem-solving exerted significant indirect effects to adherence through its substantive effects on distress. Thus, social problem-solving abilities were significantly associated with therapeutic adherence through its palliative, beneficial (and perhaps, regulatory) effects on personal stress, distress and social support.

Studies that demonstrate connections between social problem-solving abilities and objectively diagnosed biomedical variables are particularly impressive, but the lack of clarity (or, in some cases, theory) raise intrigue and speculation about the nature of these relationships. Social problem-solving abilities were significantly predictive of pressure sores diagnosed over the first 3 years of traumatically acquired spinal cord injury (SCI), and these associations were more influential than clinically important variables like severity of disability and demographic characteristics (e.g., race, gender, age; Elliott, Bush, & Chen, 2006 ). These data are among the first to document the potential of social problem-solving abilities to prospectively predict individuals who may be at risk for expensive and often preventable health complications, above and beyond the predictive value of variables deemed medically important. Nevertheless, the exact mechanisms by which problem solving exerted this observed effect cannot be determined from this study.

We can speculate from other relevant studies that problem-solving abilities may have prevented pressure sores (and promoted healthier skin) among participants in the Elliott et al. (2006) study in a couple of ways. Effective problem-solvers may have had fewer health compromising behaviors than persons who had dysfunctional styles (e.g., less sedentary, inactive behaviors, less alcohol intake; Godshall & Elliott, 1997 ); perhaps they were more successful in regulating their emotions and stress levels so they were more likely to attend to recommended regimens for skin care and maintenance (i.e., therapeutic adherence; Johnson, et al., 2006 ). However, a compelling study of glycemic control among African American men raises other possibilities.

In a study of 65 African American men with diabetes, Hill-Briggs and colleagues (2006) found avoidant and impulsive/careless styles (as measured by a short form of the SPSI-R) were significantly predictive of elevated hemoglobin A1C levels, indicative of poor glycemic control. The relationship between avoidant scores and A1C levels was not mediated by participant depression. These data are further supported by focus group research, in which a group of persons with poor glycemic control reported more avoidant and impulsive/careless responses to a problem-solving task than a group of individuals with good glycemic control ( Hill-Briggs, Cooper, Loman, Brancati, & Cooper, 2003 ). It is possible that a dysfunctional problem solving style—in the context of chronic disease and stress—may have definite correlates with impaired immune system functioning (these correlations do not permit causal explanations; glycemic control may have been influenced by unmeasured variables such as diet, exercise and distress that may, too, be influenced by problem-solving abilities).

Lessons Learned from Intervention Research

Problem-solving therapy (or training; PST) has promulgated as an attractive therapeutic option in many multidisciplinary health care settings. Indeed, the broader concept of “problem solving” is considered an essential element in chronic disease education and self-management programs ( Hill-Briggs, 2003 ). PST grounded explicitly in the principles espoused by D’Zurilla and Goldfried has been applied with notable success in alleviating distress among persons with cancer ( Nezu, Felgoise, McClure, & Houts, 2003 ; Nezu, Nezu, Friedman, & Faddis, 1998 ) and in improving coping and self-regulation skills among persons with TBI ( Rath, Simon, Langenbahn, Sherr, & Diller, 2003 ). Problem-solving interventions have documented success in individual sessions provided in primary care settings ( Mynors-Wallis, Garth, Lloyd-Thomas, & Tomlinson, 1995 ), in structured group therapy ( Rath, et al., 2003 ), in telephone sessions with community-residing adults ( Grant, Elliott, Weaver, Bartolucci, & Giger, 2002 ), and in online Web sessions for parents of children with TBI ( Wade, Corey, & Wolfe, 2006a ; and with observed benefits on child functioning, Wade, Corey, & Wolfe, 2006b ). When null effects have appeared in the peer-review literature, these may be attributable in part to a perceived lack of relevance or lack of “tailoring” of the intervention to problems—as they are perceived and experienced—of immediate concern to participants ( Shanmugham, et al., 2004 ; Study 2).

The positive effects of PST are usually ascribed to the treatment, particularly when significant increases are observed on self-appraised ( Grant et al., 2002 ) and observed problem-solving abilities ( Rath et al., 2003 ). There is some evidence that decreases in dysfunctional styles may be particularly essential in realizing significant decreases in depression ( Rivera, Elliott, Berry, & Grant, 2007 ). Participants may display increased skills in finding more solutions to their problems following PST than persons assigned to a control group ( Lesley, 2007 ). In one impressive multisite clinical trial, Sahler et al. (2005) found the beneficial effects of PST on lowering negative affect among mothers of children with cancer were pronounced among young, single mothers; Spanish-speaking mothers demonstrated continued improvements over a 3-month period. Nevertheless, there is perplexing evidence that PST can be associated with lower depression scores over time with no corresponding changes in social problem-solving abilities ( Elliott, Brossart, Berry, & Fine, 2007 ).

Critical reviews point out that this work has recurring problems with the theoretical integrity of interventions, a lack of methodological details, and a lack of clarity regarding the “dosage” sufficient for therapeutic change. Nezu (2004) has been especially critical of the lack of theoretical integrity, as the general flexibility of the original D’Zurilla and Goldfried model may be melded into or added on to any loosely defined cognitive-behavioral intervention. In some cases, it may appear that a published report used a “problem solving intervention” but there is no elaboration of principles of the model or how these were implemented in any replicable fashion (e.g., Smeets et al., in press ). There are some high-profile trials in which training in “problem solving” was presented as a marquee feature of the multisite intervention, and this evidently meant training in rational, instrumental ways to cope with certain problems, but there is no mention or recognition of the problem orientation component and its theoretical function in self-regulation and motivation (e.g., Project REACH, Wisniewski et al., 2003 ). Nezu (2004) adamantly argues that PST must address issues germane to the problem orientation component, and strategies that strictly address the problem solving skills component will not be successful.

The broad range in the number of sessions across studies frustrates our ability to determine the dosage sufficient for therapeutic change. Some studies report clinical success with after a few sessions ( Mynor-Wallis, et al., 1995 ) but other work shows no effects after two sessions administered six months apart ( Elliott & Berry, 2007 ). Weekly sessions seem to have considerable benefits over several weeks ( Grant et al., 2002 ; Rath, et al., 2003 ; Sahler et al., 2005 ). In some clinical scenarios, however, therapeutic change may occur with monthly sessions over the course of a year ( Rivera, Elliott, Berry & Grant, 2007 ). Currently, we cannot conclude from the extant literature the minimal dosage of PST sufficient to effect beneficial, therapeutic changes. This is an issue that should be addressed in future work.

A critical review of problem solving interventions for family caregivers of stroke survivors concluded that the inconsistent use of a theoretical framework and concepts, and a recurring neglect in measuring participant problem-solving abilities limits our understanding of PST in this area ( Lui, Ross, & Thompson, 2005 ). Very few of the studies reviewed used standardized measures of problem solving abilities despite their availability; many studies use the term without regard to the prevailing theoretical models and corresponding directives for training and assessment. Multidisciplinary research teams are often unfriendly to psychological theories. The Lui et al. critique reveals a high regard for cognitive-behavioral theories and a considerable respect for conducting theory-driven research and service. In particular, this critique conveys a premium on theory for organizing and interpreting multidisciplinary research, and for guiding service programs and their evaluation.

The most critical and informative review of this literature appeared in a recent meta-analysis of 31 studies of PST ( Malouff, Thorsteinsson, & Schutte, 2007 ). This paper stayed true to the basic, organizing principles of the social problem-solving model and recognized the theoretical fidelity of authors across studies. PST demonstrated a significant effect size across studies, indicating a superiority over no treatment and treatment-as-usual. Although no moderating effects were found by mode of delivery (group, individual) or in the number of hours of PST (further confounding our ability to determine adequate “dosage”), these colleagues found significant effects for the presence of problem orientation training (consistent with the Nezu position) and homework assignments. Unfortunately, they also found an “investigator” effect: Studies conducted by one of the developers of PST had a significant contribution to the overall effects of PST. This contribution was stronger than the contributions of homework assignment and problem orientation training. Finally, PST was not significantly different from bona fide treatment alternatives.

Identifying and Solving Problems in the Research Base

As these recent reviews and preceding comments attest, there are several problems that have lingered in this literature that impede our appreciation of social problem-solving abilities and the mechanisms by which they have beneficial effects on health. Yet the available research is generally supportive, as we continue to see positive and theoretically consistent findings in multidisciplinary outlets (e.g., Stroke, Journal of Behavioral Medicine, Pain, British Medical Journal, Patient Education and Counseling ) that signify an acceptance of social problem solving far beyond the usual confines of counseling psychology research (which also may signify the far-reaching impact of counseling psychology research). With these optimistic thoughts in mind, we assert the following issues should receive greater theoretical and empirical scrutiny in future work.

Utilize and Promote Theory-Driven Research and Instrumentation

Exploratory studies are unquestionably compelling and intriguing, and they arguably broaden our vision and stoke our intellectual curiosity (e.g., Hill-Briggs et al., 2006 ). But the ordinary, rank-and-file, “stopgap” studies do not advance our understanding of social problem-solving abilities if they fail to make explicit ties to the prevailing theoretical models, ignore instruments tied to these models (PSI, SPSI-R), or make vague, obscure references to “problem solving” with no appreciation for the implications of prior work, subsequently squandering the opportunity for informed, relevant research that advances existing knowledge. It is frustrating to read studies that ignore prior work, and wonder how the results could have differed if proper attention had been given to the implications of previous theory-driven research (e.g., De Vliegu, et al., 2006).

These are not trivial matters: The most egregious and harmful incidents occur in large, multisite clinical trials that purport to use “problem solving interventions” with no ties to relevant theory-driven research, and then report null effects for their intervention (as in the case of Project REACH). For those invested in policy-relevant research, small-scale studies that yield positive results are held in suspicion because smaller samples often overestimate actual treatment effects (and thereby dismiss the convergence of data across methodologically diverse studies); large-scale, multisite randomized controlled trials (like Project REACH) are assumed to be more robust, generalizeable, and necessary for determining the true efficacy of an intervention ( Califf, 2002 ). Consequently, a perceived lack of evidence from a multisite clinical trial can irreparably smear the reputation of theory-driven PST, and cultivate unjustified disinterest among funding sources and policymakers for further study of PST.

There is some concern that the primary measures of problem-solving abilities—the PSI and the SPSI-R—may be too time-consuming and cumbersome for use in many clinical settings. Interestingly, a shorter, 25-item form of the SPSI-R has been used successfully in several studies (e.g. Grant et al., 2002 ) and some researchers have read the SPSI-R aloud to participants to ensure administration (with theoretical consistently results among persons with visual impairments, Dreer et al., 2005 , and with disabling mobility impairments, Elliott, 1999 ). This may be asking too much for everyday clinical applications and shorter versions should be developed for telehealth applications and in primary care clinics. Preliminary item analysis of the SPSI-R suggests that a briefer version for greater use may be possible, with results generally consistent with contemporary reformulations of the social problem-solving model ( Dreer et al., 2007 ).

Broaden the Scope of PST across Research Teams and Clinical Settings

The effects of PST on depression and distress permeate the literature ( Malouff et al., 2007 ). Recent applications have unsuccessfully tried to use PST to elevate life satisfaction ( Rivera, Elliott, Berry, & Grant, 2007 ). More promising areas include the use of PST principles to promote healthier diets and lifestyles ( Lesley, 2007 ; Perri et al., 2001 ) and to facilitate the use of problem-solving strategies in social interactions (essential for community reintegration; Rath et al., 2003 ). Although much of this work is hampered by the lack of specificity about the actual implementation of PST and relevant theory (rendering the results suspect and thwarting generalizability and replicability; e.g., Van den Hout et al., 2003 ), these studies collectively illustrate the potential of PST in various applications. Other colleagues, for example, incorporate PST in promoting healthier lifestyles (including matters of impulse control, adherence, mood regulation) among persons who are HIV+ (the Health Living project, Gore-Felton et al., 2005 ) and who have substance abuse histories ( Latimer, Winters, D’Zurilla, & Nichols, 2003 ). PST may prove to be quite adaptable in long-distance, community-based telehealth programs, in which ongoing services may be provided to underserved people and to those in remote areas (e.g., Grant et al., 2002 ; Wade et al., 2006a ).

Identify the Mechanisms of Therapeutic Change

It appears that there is no clear evidence of the “dosage” of PST necessary to effect change. Moreover, when change occurs, it is unclear if the changes are uniquely attributable to PST. One persistent issue concerns the intricate relationship between a negative orientation and self-report measures of distress. Even when we find evidence linking effective problem-solving abilities with objectively defined outcomes (e.g., skin ulcers), we do not know if effective problem-solving abilities influenced greater behavioral adherence to therapeutic regimens, or if the problem orientation component was instrumental in regulating emotional adjustment and prevented distress that could have compromised health. We do know that PST is more successful when the issues germane to the problem orientation component are addressed, and there is evidence that decreases in negative orientation and dysfunctional problem-solving styles can be associated with decreases in depression in response to PST ( Rivera, Elliott, Berry, & Grant, 2007 ).

There is legitimate concern that—with respect to social problem-solving abilities—the “absence of the negative” may be more powerful than the “presence of the positive.” It is important for us to understand how and why a negative, dysfunctional style is associated with negative outcomes (and a greater likelihood of a positive outcome), and why and under what conditions a constructive problem-solving style proves uniquely beneficial. This could entail studies of social problem solving abilities and biomedical indicators of stress and adjustment. We believe this is a pressing issue given current interest in social problem-solving as an important variable in positive psychology ( Heppner & Wang, 2003 ).

Attend to Matters of Diversity

Few cognitive-behavioral variables appear to be as culturally resilient as social problem-solving abilities ( Heppner et al., 2004 ). Large-scale studies that have controlled for possible effects of ethnicity have shown the relationships of social problem-solving abilities to distress and adherence ( Johnson et al., 2006 ) and to health outcomes ( Elliott et al., 2006 ) are not mediated by race. Studies of race-specific issues have yielded some of the most intriguing data to date among problem-solving and biomedical markers of health (among African-American men; Hill-Briggs, et al., 2006 ); other work has shown some effects for PST tailored to address health promotion issues among african Americans with hypertension ( Lesley, 2007 ). There is also some indication that Spanish-speaking participants may experience greater benefits from PST than others ( Sahler, et al., 2005 ).

There are many health problems that are disproportionately experienced by ethnic minorities in the United States (e.g., diabetes, stroke, disability incurred in acts of violence). Collectively, available evidence suggests that PST may be used in prevention and remedial programs to assist persons from minority backgrounds who live with these conditions. Although this work is promising, we have yet to see robust effects of PST across health conditions and research has yet to be conducted in any substantive fashion with certain ethnic groups (e.g., Chinese, although initial work has been consistent with extant theoretical models; see Siu & Shek, 2005 ). Ideally, the next wave of intervention research will document effects of PST among people across ethnic groups and cultures.

Problem Solving for the People

Research to date suggests that PST can be effectively provided by psychologists, physicians, nurses and counselors. As the needs of our society demand greater attention to and support for the increasing number of people who live with a chronic health conditions that necessitates routine adherence to prescribed regimens (and currently this number constitutes almost 50% of the population of the United States; Partnerships for Solutions, 2004 ), health promotion programs will increasingly rely on paraprofessionals and community health workers to reach a larger number of individuals. These public health efforts already work with community groups (schools, churches) and with respected paraprofessionals within certain communities (e.g., promotoras in Latino communities) to educate people about health and health promotion skills. We believe problem-solving principles can be taught in public health interventions to reach a greater percentage of people who are affected by chronic health conditions (including family members of an individual with a diagnosable condition). We also know that PST can be effectively provided in the community via telehealth, so a greater use of existing technologies is expected in community-based programs. PST can be a useful modality for prevention programs for teaching health promotion skills (e.g., nutrition, sexual health and behaviors, exercise and activity) to individuals, generally.

A real concern lurking in this sea of possibility is the difficulty in determining when and how to best apply PST: People experience a wide range of problems in our communities, and paraprofessionals may be overwhelmed by the depth and severity of certain problems they will inevitably encounter in their clientele. Furthermore, we know that some individuals live with considerable distress and face many problems that have a restricted range of options and solutions. In these clinical scenarios, a strict reliance on the rather linear application of PST principles may be frustrating to paraprofessionals and clients. Research is needed to determine the best and optimal use of PST by paraprofessionals in public health interventions, and when doctoral-level providers are best suited for using PST in more complex cases that demand greater clinical expertise.

The study and application of social problem-solving abilities has matured beyond its early years in the counseling psychology literature to be embraced by a larger, multidisciplinary audience. Many theoretical issues remain for counseling psychologists to examine and refine, and an influx of new researchers would do much to assuage concerns of “investigator” effects in PST research. Perhaps the next wave of PST research will be conducted in public health programs. It behooves counseling psychology to be involved in this activity so that the theoretical tenets of social problem-solving are accurately integrated and realized in this work, and in the process, ensure a more accurate realization of the effects and applicability of social problem-solving theory and research for the public good.

Acknowledgments

This chapter was supported by grants to the first author awarded by the National Institute on Child Health and Human Development (#T32HD07420), the National Institute on Disability and Rehabilitation Research (H133A020509), and from the National Center for Injury Prevention and Control (#R49/CE000191) to the Injury Control Research Center at the University of Alabama at Birmingham.

The contents of this study are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies.

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Group Collaboration Play (GPS) & Problem Solving Scale for Assessment

We Thinkers Series

Authors: Michelle Garcia Winner , Nancy Tarshis , Kari Zweber Palmer , Ryan Hendrix

Children bring different levels of social competencies into peer-based group collaboration and play. Assessing these in children ages 4-7 is important for understanding students’ social emotional strengths and weaknesses and designing relevant treatment programs. GPS is a qualitative, observation-based assessment tool to help professionals evaluate the peer-based play abilities of children as part of their dynamic social assessment. Useful to IEP teams (parents too) in generating definable social learning goals and objectives, GPS is also a tool for researchers.

• Ages: Ages 4-7 Assessment
• Format: Paperback
• ISBN: 9781936943364
• Published: 2018

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