Downtime after Critical Incidents in Emergency Medical Technicians/Paramedics
Bibliographic record
Abstract
Effective workplace-based interventions after critical incidents (CIs) are needed for emergency medical technicians (EMT)/paramedics. The evidence for a period out of service post-CI (downtime) is sparse; however it may prevent posttraumatic stress disorder (PTSD) and burnout symptoms. We examined the hypothesis that downtime post-CI is associated with fewer symptoms of four long-term emotional sequelae in EMT/paramedics: depression, PTSD, burnout, and stress-related emotional symptoms (accepted cut-offs defined high scores). Two hundred and one paramedics completed questionnaires concerning an index CI including downtime experience, acute distress, and current emotional symptoms. Nearly 75% received downtime; 59% found it helpful; 84% spent it with peers. Downtime was associated only with lower depression symptoms, not with other outcomes. The optimal period for downtime was between <30 minutes and end of shift, with >1 day being less effective. Planned testing of mediation of the association between downtime and depression by either calming acute post-CI distress or feeling helped by others was not performed because post-CI distress was not associated with downtime and perceived helpfulness was not associated with depression. These results suggest that outcomes of CIs follow different pathways and may require different interventions. A brief downtime is a relatively simple and effective strategy in preventing later depression symptoms.
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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 | 0.008 |
| 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 | 0.001 |
| 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".