A Qualitative Evaluation of the Impacts of COVID-19 on Canadian Public Safety Personnel Health and Wellbeing
Bibliographic record
Abstract
Public safety personnel (PSP) are known to experience difficult and demanding occupational environments, which were further complicated by the COVID-19 crisis. While public safety research typically focuses on the impact of operational stressors on PSP functioning and wellbeing, relatively less is known about the types and impacts of organizational stressors and how all these affect social wellbeing during the pandemic. The current study surveyed Canadian firefighters (n = 123), paramedics (n = 246), and public safety communicators (n = 48) that continued to serve the public over the course of the pandemic. Participants responded to two open-ended survey questions about how COVID-19 affected their lives at work and home. Using an inductive thematic analysis approach, responses were coded to identify emergent, data-driven themes while drawing on existing theory for analysis. Across occupational groups, qualitative analyses revealed that the public safety measures imposed by the COVID-19 pandemic further exacerbated existing operational and organizational strains, including increased exposure to distressing calls, absenteeism and coping with alcohol, and a lack of support from management. Participants also identified financial strain and housing insecurity as stressors, as well as frustration and helplessness at others’ non-compliance with public health advisories and protocols. Communication surrounding the rationale behind government decision-making, the efficacy of serology tests, and rates of infection were also identified. Together, these findings offer a nuanced understanding of the interplay among operational, organizational, and social stressors experienced by Canadian PSP during the COVID-19 pandemic, illuminating their impact on mental health and wellbeing, and identifying targeted areas of focus for future planning and meaningful intervention to support PSP wellness.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".