Risk factors for psychological distress following injury
Bibliographic record
Abstract
OBJECTIVES: To identify predictors of psychological morbidity among injured patients admitted to an Emergency Department (ED). DESIGN: A prospective cohort study. PARTICIPANTS: Participants were consecutive male ED attenders. 210 (97.7%) patients consented to participate. At one month, 128 (61.0%) responded, at six months, 114 (54.3%), at eighteen months 96 (45.7%). MAIN OUTCOME MEASURES: Measures immediately following injury were the Hospital Anxiety and Depression Scale, the Eysenck Personality Questionnaire and the McGill pain questionnaire. Recovery at one month was recorded using the SF-36 Health Survey, COPE scale, Perceived Stress Scale and Revised Impact of Events Scale. At six and eighteen months outcome was measured using the General Health Questionnaire (28 items) and Revised Impact of Events Scale. Multivariate analysis identified pre-morbid, accident-related and recovery factors influencing outcome at six and eighteen months. RESULTS: The strongest predictors of outcome were initial levels of anxiety and depression, prior history of mental health problems, early PTSD symptoms and involvement in litigation. These factors predicted between 40-60% of the variance at six months (p<0.001), and 50-60% of the variance in psychological distress at eighteen months (p<0.001). CONCLUSION: Factors identifying individuals at-risk from psychological distress following injury include those related to the immediate response and the recovery phases of injury. Further development is needed to convert identified predictors into a comprehensive screening tool for clinical use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".