A count of coping strategies: A longitudinal study investigating an alternative method to understanding coping and adjustment
Bibliographic record
Abstract
Researchers recently have suggested that coping flexibility (i.e., an individual's ability to modify and change coping strategies depending on the context) may be an important way to investigate coping. The availability of numerous coping strategies may be an important precursor to coping flexibility, given that flexibility can only be obtained if an individual is able to access and use different coping strategies. Typically, studies examining the use of coping strategies compute means-based analyses, which assess not only what strategies are used but also how much they are used. Thus, there is limited ability to differentiate between individuals who use a lot of strategies infrequently, and individuals who use only one or two strategies a lot. One way to address this confound is to count the number of strategies that an individual uses without attention to how frequently they use them (i.e., a count-based approach). The present longitudinal study compares a count-based model and a means-based model of coping and adjustment among undergraduates (N = 1132). An autoregressive cross-lagged path analysis revealed that for the count-based approach, using a greater number of positive coping strategies led to more positive adjustment and less suicide ideation over time than using a smaller number of positive coping strategies. Further, engagement in a greater number of negative coping strategies predicted more depressive symptoms and poorer emotion regulation over time. In comparison, the means-based model revealed identical results for negative coping strategies; however, engagement in more frequent positive coping strategies did not predict better positive adjustment over time. Thus, a count-based approach offers a novel way to examine how the number of coping strategies that individuals use can help promote adjustment among university students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".