Protocol for a national study on emergency response team officers' mental health and well-being: working with police services in Canada
Bibliographic record
Abstract
Purpose The purpose of our article is to explicate how researchers in Canada can learn from our methodology to work in collaboration with police services and foster research collaborations with “hard-to-reach” groups. Police ERTs have been criticized for being “secretive,” “elite,” distrusting of popular media or outside research, and difficult to contact. Subsequently, researchers often struggle to form partnerships with police agencies to access information regarding ERT data – leading the large majority of research in Canada to rely on mixed samples or secondary data like Access to Information and Privacy Requests (ATIP) or Freedom of Information Requests (FOI) to make bold claims about the state of ERT work in Canada. As such, we provide insights into how we employed study tools and collaborative efforts, including virtual interviewing, which fosters disinhibition among participants to assist in sharing distressing realities, to reach ERTs on a national scale, forming one of the largest qualitative research designs involving ERT in Canada. Design/methodology/approach The current article is a methodological protocol article that explicates our qualitative approach, procedures and ethical decision-making tied to our national Canadian sample exploring the mental health and well-being of police emergency response team (ERT) officers. In the current protocol article, we explain our methodological and qualitative procedures for collecting interview data from part-time and full-time ERT members (n = 117) across a two-year data-collection period in Canada. Respondents come from 20 separate ERTs from 17 unique police services nationally. Thus, we lay out our processes in creating the potentially largest study of ERT in Canada, perhaps even internationally. Findings Because this is a methodological protocol article, no empirical findings were produced. However, we clearly explicate our best practices and methodological approaches to how we collected 117 semi-structured interviews with police services from across Canada. Particular focus is placed on our approach to fostering research collaborations with police services, including but not limited to our iterative ethical choices and pathways forward for research in the field. Our findings are earmarked for other qualitative researchers who are also interested in forming national research projects with police services, as we illuminate how we managed to formulate one of the largest qualitative research designs on ERTs in Canada. Originality/value The current study explicates our procedures in how we conducted one of the first, if not the first, research projects in Canada to qualitatively explicate how help-seeking and pathways to care are different, both good and bad, across provincial, municipal and federal tactical teams, inclusive of factors about workplace cultures, politics, material and human resourcing, access to psychological services, occupational, organizational and operational stress, and stigma. Our article provides pathways forward for other qualitative researchers on how to access, produce and manage a large-scale research project with hard-to-reach public safety personnel, including an emphasis on flexible data collection procedures.
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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 | 0.051 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.094 | 0.012 |
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".