Assessments of Coping After Acquired Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To identify measures of coping styles used by patients with acquired brain injury; to evaluate the conceptualization, feasibility, and psychometric properties of the instruments; and to provide guidance for researchers and clinicians in the choice of a suitable instrument. DESIGN: Systematic review. RESULTS: The search identified 47 instruments, of which 14 were selected. The instruments focused on dispositional coping, situation-specific coping, or domain-specific coping. Psychometric properties were scarcely investigated. The COPE stood out in terms of psychometric properties but had low feasibility. The brief COPE, Coping Scale for Adults-short form, and Utrecht Coping List stood out in terms of feasibility, and the available psychometric properties of these instruments were good. Only the Coping With Health Injuries and Problems was used as other report. CONCLUSION: Information on psychometric properties of coping instruments in acquired brain injury is scarcely available and limits the strength of our recommendations. For patients with mild injuries, we cautiously recommend the COPE and for patients with more severe injuries the brief COPE, Coping Scale for Adults-short form, Utrecht Coping List, and Coping With Health Injuries and Problems-other-report. Other instruments may be used to address particular issues such as coping with a specific stressful situation or illness.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".