Bibliographic record
Abstract
Public safety employees are vulnerable to psychological health concerns from exposure to operational (critical incidents) and organizational (e.g., workload, supervisor conflict) stressors layered over personal life stressors (e.g., health issues, relationship conflict). Organizational leadership has a responsibility to promote a psychologically healthy work environment to mitigate workplace stress and ensure employees have access to effective prevention and intervention supports to minimize the risk of long-term compromised mental health and the associated potential impact on employee conduct (e.g., absenteeism, public complaints, errors in judgment). A major barrier to strategic planning to best achieve organizational wellness goals is insufficient knowledge about the sources of stress and psychological health needs of one’s organization. Evidence-informed decision-making requires that organizations first identify and understand the problem before implementing strategies to promote psychological health and the associated positive work outcomes; otherwise, implemented strategies may miss the mark and achieve little in terms of desired impact. Drawing from our experience working with three Atlantic Canadian public safety organizations representing police, fire, and public safety communicator services, we describe a research-informed psychological health survey process framework to guide organizations on (a) what knowledge they should be seeking and (b) how to best to implement a survey about the psychological health needs of their employees, as well as identify strengths/gaps in existing support services and operational processes affecting employee wellness. We conclude with a discussion of the essential role of organizational management and employee leaders in maximizing the success of the survey and the implementation of subsequent wellness initiatives.
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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.123 | 0.039 |
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".