What influences safety in paramedicine? Understanding the impact of stress and fatigue on safety outcomes
Bibliographic record
Abstract
Objective The purpose of this study was to build on extant research linking fatigue to safety outcomes in paramedicine by assessing the influence of a multiplicity of workplace stressors, including chronic and critical incident stresses on safety outcomes. Methods A cross-sectional survey was deployed to 10 paramedic services in Ontario. Validated survey instruments measured operational and organizational chronic stress, critical incident stress, post-traumatic stress symptomatology (PTSS), fatigue, safety outcomes, and demographics. Analysis of covariance assessed associations of workplace stresses with safety outcomes and corroborated findings using hierarchical linear model and generalized estimating equations (GEE) by taking into account paramedic service when assessing the proposed associations. A non-responder survey was conducted to asses for demographic differences in those who did and did not complete the survey. Results This survey had a response rate of 40.5% (n = 717/1767); 80% of paramedics reported an injury or exposure to pathogen, 95% reported safety compromising behaviors, and 76% reported medical errors. In the GEE analyses, paramedic injury was significantly related to fatigue (0.13, SE = 0.06, P = 0.020), critical incident stress (0.03, SE = 0.01, P < 0.01), and PTSS (0.03, SE = 0.01, P < 0.01). Safety compromising behaviors were significantly associated with fatigue (0.37, SE = 0.06, P < 0.01), organizational stress (0.06, SE = 0.01, P < 0.01), and critical incident stress (0.01. SE = 0.01, P = 0.017). Medication errors were significantly related to fatigue (0.12, SE = 0.05, P < 0.01). Finally, the bivariate analysis showed increased stress factors and fatigue was associated with increased safety outcomes. Conclusion These findings illustrate that a host of different stressors may influence safety-related behaviors. For those interested in safety, these findings point to the need for a holistic focus on fatigue and stress in paramedicine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".