Destress 9-1-1—an online mindfulness-based intervention in reducing stress among emergency medical dispatchers: a randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: Emergency medical dispatchers (EMDs) experience significant stress in the workplace. Yet, interventions aimed at reducing work-related stress are difficult to implement due to the logistic challenges associated with the relatively unique EMD work environment. This investigation tested the efficacy of a 7-week online mindfulness-based intervention (MBI) tailored to the EMD workforce. METHODS: Active-duty EMDs from the USA and Canada (n=323) were randomly assigned to an intervention or wait list control condition. Participants completed surveys of stress and mindfulness at baseline, post intervention, and 3 months follow-up. Repeated measures mixed effects models were used to assess changes in stress and mindfulness. RESULTS: Differences between the intervention group and control group in pre-post changes in stress using the Calgary Symptoms of Stress Inventory were statistically significant, with a difference of -10.0 (95% CI: -14.9, -5.2, p<0.001) for change from baseline to post intervention, and a difference of -6.5 (95% CI: -11.9, -1.1, p=0.02) for change from baseline to 3 months follow-up. Change in mindfulness scores did not differ between groups. However, increases in mindfulness scores were correlated with greater reductions in stress for all participants, regardless of group (r=-0.53, p<0.001). CONCLUSIONS: Development of tailored online MBIs for employees working in challenging work environments offer a promising direction for prevention and intervention. This study found that a short, weekly online MBI for EMDs resulted in reductions in reports of stress. Implications of online MBIs in other emergency responding populations and directions for future research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.100 | 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 teacher head, 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".