Interventions for critical incident stress in emergency medical services: a qualitative study
Bibliographic record
Abstract
Abstract Controversy over the use of Critical Incident Stress Debriefing leaves Emergency Medical Services (EMS) organizations with little direction in preventing sequelae of Critical Incident Stress (CIS) in their employees. Objectives of the study were to explore and describe Emergency Medical Technicians' (EMTs) experiences of critical incidents and views about potential interventions, in order to facilitate development of interventions that take into account EMS culture. We interviewed 60 EMT practitioners and supervisors, and examined interview transcripts using ethnographic content analysis. EMT practitioners want emotional support in their workplace soon after a critical incident, and welcome interventions that would enhance this. They also experience a brief timeout as important in preventing sequelae of CIS. Exchanges with supervisors and peers that are experienced as supportive are illustrated. Barriers to support are described, as well as ways to address them. Educating supervisors and front‐line practitioners to recognize and respond supportively to critical incidents is acceptable to them. However, an organizational culture that stigmatizes vulnerability is the most insidious and challenging barrier to accessing support after a critical incident. Addressing the issue of stigma is critical to developing appropriate interventions. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".