An Exploration of Encounters Between People with Lived Experience of Mental Illness and Police Officers
Bibliographic record
Abstract
Understanding the ways police officers and people with lived experience of mental illness interact during mental health calls is imperative to improving the outcomes of these encounters. Despite increased attention and public calls for change, little is known about the complex ways police officers and people with mental illness interact during a mental health crisis. To address the paucity of literature, this study sought answers to critical and under-explored areas to better understand the context and characteristics of these interactions. The overarching research question for this study was: How do people with mental illness and police officers experience interacting with one another during a mental health crisis? Specifically, this study asked participants to expand on their descriptions of the context and characteristics of incidents, interventions and outcomes, their perceptions of officer roles, their perception of the dangerousness of these encounters, and what they want others, including each other, to know. This study used in-depth interviews conducted with 18 participants from across Canada including 13 people with lived experience of mental illness, and five police officers. Transcribed interviews were analyzed using NVivo version 12 software. Findings from this study illustrate the complexity and diversity of these interactions as well as the similarities and differences in experiences described by both groups. Findings from this study can be used to further develop policy, practice, and research, that includes people with lived experience of mental illness in meaningful ways.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".