Planful<i>versus</i>Avoidant Coping: Behavior of Individuals With Moderate-to-Severe Traumatic Brain Injury During a Psychosocial Stress Test
Bibliographic record
Abstract
Many people who sustain traumatic brain injuries (TBI) have poor psychosocial outcomes that have been related to the use of avoidant coping. A major obstacle to understanding the mechanisms of this relationship are the self-report measures by which coping has been traditionally evaluated. The purpose of the present study was to compare coping behavior during a simulated real-world stress test with self-reported coping. People with moderate-to-severe TBI and matched controls completed the Baycrest Psychosocial Stress Test (BPST) where coping behavior was evaluated, and also completed the Ways of Coping Questionnaire (WOC). While there were no group differences in self- or significant-other-reported behavior on the WOC, the TBI group engaged in more avoidant than planful behavior on the BPST, while the control group displayed the opposite pattern of behavior. Moreover, in the control group there were positive relations between behavior on the BPST and self-reported coping on the WOC, but no such relation within the TBI group. Secondary analyses allowed for TBI participants to be characterized as "planners" or "avoiders." This is the first study, to our knowledge, to report behavioral differences in coping post-TBI. Future work investigating the moderators of these differences may have significant implications for rehabilitative intervention.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".