Posttraumatic Symptoms and Disability in Paramedics
Bibliographic record
Abstract
OBJECTIVE: The concern that secondary gain may result in an overreporting of trauma symptoms in those seeking compensation or taking stress leave from work has raised questions about the relation between posttraumatic stress and disability. This study attempts to examines the relation between traumatic stress symptoms and the use of work leave in an anonymous sample of emergency-service workers who are not currently seeking compensation. METHOD: A total of 86 paramedics completed questionnaires that addressed exposure to traumatic events, use of mental health stress leave, social support, current level of distress, and personality patterns. Comparisons were made between groups who had used mental health stress (MHS) leave and those who had not. Logistic regression was used to determine the best predictors of using leaves. RESULTS: Current levels of social support were associated with previous use of mental health stress leave. In addition, significantly more individuals who had taken MHS leave in the past reported posttraumatic stress symptoms in the high or severe range. People with personality patterns characterized by suspiciousness, hostility, and isolation and having a tendency toward demanding, controlling, and manipulative behaviour in relationships were also more likely to have taken an MHS leave. CONCLUSION: Although social support and trauma symptoms were associated with the use of MHS leave, in this study, personality style was the strongest factor differentiating those individuals who took MHS leave from those who did not.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".