Physicians’ perspectives on processes for emergency mental health transfers from university health clinics to hospitals in Ontario, Canada: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, there is variability in how students experiencing a mental health crisis are transferred from university health clinics to hospital for emergent psychiatric assessment, particularly regarding police involvement and physical restraint use. We sought to understand existing processes for these transfers, and barriers to and facilitators of change. METHODS: Between July 2018 and January 2019, we conducted semistructured qualitative interviews by telephone or in person with physicians working at Ontario university health clinics. We developed the interview guide by integrating an extensive literature review, and the expertise of stakeholders and people with lived experience. We analyzed the interview transcripts thematically. Analysis was informed by participant responses to a questionnaire exploring their perspectives about crisis transfer processes. We requested institutional policy and process documents to support analysis and generate a policy summary. RESULTS: Eleven physicians (9 family physicians and 2 psychiatrists) from 9 university health clinics were interviewed. Ten of the 11 completed questionnaires. Policy and process documents were obtained from 5 clinics. There was variation in processes for emergency mental health transfers and in clinicians' experiences with and beliefs about these processes. Police were commonly involved in transfers from 7 of the 11 clinics, and in nearly all or all transfers from 5 of the 11 clinics. Handcuffs were always or almost always used during transfer at 2 clinics. Three major themes were identified: police involvement and restraint use can cause harm; clinical considerations are used to justify police involvement and restraint use; and pragmatic, nonclinical factors often inform transfer practices. INTERPRETATION: The involvement of police and use of restraints in crisis mental health transfers to hospital were related to pragmatic, extramedical factors in some university health clinics in Ontario. Exploring existing variability and the factors that sustain potentially harmful practices can facilitate standard implementation of less invasive and traumatizing transfer processes.
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".