PTSD, Acute Stress, Performance and Decision-Making in Emergency Service Workers.
Bibliographic record
Abstract
Despite research identifying high levels of stress and traumatic stress symptoms among those in the emergency services, the impact of these symptoms on performance and hence public safety remains uncertain. This review paper discusses a program of research that has examined the effects of prior critical incident exposure, acute stress, and current post-traumatic symptoms on the performance and decision-making during an acutely stressful event among police officers, police communicators, paramedics and child protection workers. Four studies, using simulation methods involving video simulators, human-patient simulators, and/or standardized patients, examined the performance of emergency workers in typical workplace situations related to their individual profession. Results varied according to level of acuity of stress and the nature of performance and decision-making. There was no evidence that PTSD had a direct impact on global performance on tasks for which emergency responders are highly trained. However, PTSD was associated with assessment of risk in situations that required professional judgement. Further, individuals experiencing PTSD symptoms reported higher levels of acute stress when faced with high acuity situations. Acute stress in these studies was associated with performance deficits on complex cognitive tasks, verbal memory impairment and heightened assessment of risk.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".