Police need wellness checks too: Embedding a culture of wellness and resilience in policing
Bibliographic record
Abstract
The policing profession is in the midst of a global mental health and wellness crisis (Edwards, 2023).The police workforce (including police officers and non-uniform members) serves as the backbone to maintaining community safety and well-being; however, sentiments across the profession point to an overwhelming sense of stress, burnout, and mental healthrelated issues.Anxiety, depression, alcohol and substance abuse, suicide, and post-traumatic stress disorder are being reported at alarming rates among police service members (Tam-Seto & Thompson, 2023).A Canadian study showed that 37% of police officers at the municipal and provincial level and 50% of police officers at the federal level reported having a mental health disorder (Carleton et al., 2017, as cited in Grupe, 2023).These are just disclosed statistics.The mental health and wellness of the workforce is not a sector-specific issue; it is a human issue-one facing every single police service in Canada and, indeed, globally.We sponsored the first special edition of the Journal of Community Safety & Well-Being focused on "Envisaging the Future," because we recognize that you cannot have safe, healthy, and resilient communities without a safe, healthy, and resilient police workforce.Full stop.Wellness, focused on health promotion and disease prevention, is foundational to a sustainable model of policing that supports the health and safety of our communities.Recognizing this is a complex issue that will not be solved with simple solutions, our Security and Justice and Health Care practices have come together to co-sponsor this second special edition focused on wellness and resilience in policing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".