Mental Health and Well-Being among Children of Public Safety Personnel in Canada
Bibliographic record
Abstract
Public safety personnel (PSP) often experience stress due to their occupational demands that affect the family environment (e.g., work-family conflict, marital breakdown, disruption to home routines, and holidays). A substantial base of research has focused on the impact of PSP work on the marital relationship, but fewer studies have focused specifically on children’s functioning within PSP families. The current study investigated mental health, well-being, and functioning among children of PSP in Canada, as reported by PSP. Data were collected between 2016 and 2017 as part of a large pan-Canadian study of PSP. Participants (n = 2092; 72.5% women) were PSP parents who responded to questions about their 4- to 17-year-old children. Overall, a substantial proportion of PSP parents reported their children have at least some difficulties with sadness (15.4%), worries and fear (22.0%), disobedience or anger (22.0%), attention (21.0%), and friendships (11.4%). Firefighters reported the fewest problems among their children compared to other PSP groups. Almost 40% of participants indicated that their child’s problems were related to their work as a PSP. The results highlight the need to find ways to identify children that are struggling and provide support to those families. Organizations and PSP leadership should develop and prioritize efforts to support families of PSP members, with the likely outcome of enhancing PSP member well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".