‘I’m not sick!…Are you?’ Groupthink in police services as a barrier to collecting mental health data
Bibliographic record
Abstract
Despite the high prevalence of mental disorders among Canadian police officers, treatment-seeking is lower than expected. Toward understanding how mental health services can be tailored for higher utilization by police, we aim to uncover factors that contribute to stigma and barriers to use, specifically within the context of group dynamics between officers. Nine semi-structured focus groups and one interview were conducted with civilian and non-civilian police service employees in Ontario, Canada. Data were coded to allow for themes to emerge from the transcripts. Participant voices ( n=33) revealed the presence of three characteristics of Janis’ groupthink: high group cohesion, conditions that create high stress and low self-esteem, and operating under directive leadership; each creating pressures that serve as barriers to treatment-seeking [Janis IL (1972) Victims of Groupthink: A Psychological Study of Foreign-Policy Decisions And Fiascoes. Boston: Houghton Mifflin]. Groupthink offers a potential explanation about why police, despite a high prevalence of mental disorders and access to mental health services, do not seek treatment as expected. Janis’s theory of groupthink is supported by police officer dialogue in focus groups. Understanding police group interactions can better inform prevention and treatment programs, ultimately leading to better access and use of existing mental health services, a reduction in stigma associated with treatment-seeking, and a healthier police workforce.
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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 | 0.107 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| 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".