Coping behaviour following traumatic brain injury: What makes a planner plan and an avoider avoid?
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Avoidant coping has consistently been related to negative outcomes following traumatic brain injury (TBI), although the mechanisms of this relationship are not clear. A recent study demonstrated that people with moderate-to-severe TBI engaged in more avoidant than planful coping behaviour during a psychosocial stress test, while their matched healthy counterparts engaged in the opposite pattern. The purpose of the current study was to evaluate the neuropsychological, physiological and psychological differences between planners and avoiders with TBI. METHODS AND PROCEDURES: Eighteen people with moderate-to-severe TBI completed the Baycrest Psychosocial Stress Test (BPST) where coping behaviour was evaluated and physiological measures recorded. Participants also completed a series of questionnaires and a neuropsychological test battery. MAIN OUTCOMES AND RESULTS: Compared to avoiders, planners had better executive function, were more psychologically and physiologically reactive and performed better on the BPST. Dysfunction on tests assessing executive abilities was the best predictor of avoidant coping, while physiological and psychological reactivity were the best predictors of planful coping. CONCLUSIONS: This study is the first to document differences between planners and avoiders with TBI. Understanding the determinants of coping following TBI will allow for more sophisticated and targeted 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".