Coping profiles across adulthood: findings from a 3-wave longitudinal study using latent profile transition analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The use of various coping strategies has important impacts on individuals' health and well-being. However, most of the coping literature continues to use variable-centred approaches that ignore unique within-person interactions among coping strategies, as well as change in these interactions over time. The present study sought to address these gaps by identifying coping profiles representing distinct interactions between a set of coping strategies and examining the stability of these profiles over time. DESIGN AND METHODS: The study used data from a large community sample of Canadians (N = 1,372) who completed the short form for the Coping Inventory for Stressful Situations (CISS-SF) scale at three time-points or waves over 5 years. Latent profile transition analysis (LPTA) was used to identify latent profiles and then examine the stability of the profiles over time. RESULTS: LPTA revealed three distinct coping profiles: Engaged, Avoidance-Oriented, and Disengaged. All coping profiles showed relatively strong stability across the three waves, with Engaged coping being the most stable over time. CONCLUSIONS: These findings have important implications for future coping research using a person-centred approach, including for the identification of individuals at risk for poor life outcomes due to reliance on these coping profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".