Police Attitudes Toward Seeking Professional Mental Health Treatment
Bibliographic record
Abstract
Abstract Public safety personnel (PSP), including police officers, often work in high-stress environments that expose them to potentially psychologically traumatic events (PPTE). As a result, PSP are at a higher risk than most other occupational roles for the development of new or worsening mental health concerns, such as posttraumatic stress disorder (PTSD), major depressive disorder, general or social anxiety disorder, and substance use disorders (Carleton et al., Canadian Journal of Psychiatry 63(1):54–64, 2018; Haugen et al., Journal of Psychiatric Research 94:218–229, 2017; Velazquez and Hernandez, Policing: An International Journal 42(4):711–724, 2019). Given these higher rates, research examining how to support the mental health of individuals in these occupations and how to improve our understanding of mental health help-seeking beliefs is critical. Consequently, the overall objective of the current study was to examine predictors of help-seeking attitudes among a group of police officers, while accounting for the effects of gender, years of employment, type of training experience, and mental health status (i.e. presence of mental illness, perceived resilience) on mental health-related help-seeking behaviours. A total of 112 police officers in a mid-sized law enforcement organization in Ontario, Canada, completed an online survey as part of a larger study exploring their mental health. Results indicate that years of experience and mental health training may improve attitudes toward seeking help for mental health. Our discussion offers suggestions for law enforcement organizations to consider to encourage their members to seek help for mental health difficulties.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".