Emergency Transport Crew: Post-Traumatic Stress Disorder Prevention Program
Bibliographic record
Abstract
Background: The incidences of post-traumatic stress disorder (PTSD) among critical care nurses, emergency room nurses, and paramedics range from 20 to 33%. PTSD is associated with a lower quality of life (QOL), occupational impairments, physical health decline, and increases the risk of premature death. Research supports prevention and surveillance measures for post-traumatic stress disorder in emergency medical service providers, but the practice is not routinely done. Methods: A multi-purpose quality improvement project focused on educating transport crew members about PTSD. Other interventions emphasized anti-stigma lessons, resiliency assistance, and coping skills training. The pilot provided surveillance efforts, employed an early organizational PTSD recognition, and immediate debriefing for at-risk personnel at three Air Evac Lifeteam bases. Results: After the QI interventions, most crew members’ overall post-test PCL-5 scores were lowered by 12.5%. Another measure of the QI success was the Professional Quality of Life score improvement. Specially, the compassion satisfaction average level increased by 14% and the average burnout level decreased by of 15%. Conclusions: The QI project demonstrated the transport crew members’ well-being can be positively influenced by a PTSD prevention and surveillance program. These interventions offer a promising reduction in the prevalence of stress and PTSD. A nationwide practice change with these project interventions could improve the mental health of helicopter emergency medical personnel.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".