Basic Dimensions of Resilient Coping in Paramedics and Dispatchers
Bibliographic record
Abstract
Introduction Paramedics and dispatchers are exposed to high levels of stress and consequent psychological injury. Resilience training enhances the capacity to cope with stress and be resilient. It is widely recommended that resilience training be customised to specific occupational groups, but there is no established method for achieving such customisation. Exploratory factor analysis was used to identify dimensions underlying resilient coping in paramedics and dispatchers. The objective was to provide a basis for customised resilience training in this population. Methods The Resilient Coping Survey (RCS) was developed on the basis of interviews with paramedics and dispatchers as well as scoping review to identify coping items relevant to this occupational group. The RCS included scales of coping (Resilience at Work and the Self-Compassion Scale – Short Form), a scale of self-perceived resilience (the Brief Resilience Scale) and a set of items reflecting coping skills specific to paramedic service work. The survey was administered to paramedics and dispatchers in British Columbia, Canada. Results 703 paramedics and dispatchers responded to the survey. Analysis of the survey data identified five resilient coping factors: balance, self-acceptance, trusted social support, meaningful work and physical self-care. Each of these factors predicted resilience. No difference was found overall in resilience across gender; but only for male workers did resilience fall steadily with years of service. Conclusion Resilience training for paramedics and dispatchers would appropriately target the five resilient coping factors and be delivered throughout the paramedic service career.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".