Who is reported missing from Canadian hospitals and mental health units?
Bibliographic record
Abstract
Purpose International literature on missing persons suggests that a significant volume of missing person cases originate from hospitals and mental health units, resulting in considerable costs and resource demands on both police and health sectors (e.g., Bartholomew et al. , 2009; Sowerby and Thomas, 2017). In the Canadian context, however, very little is known about patients reported missing from these locations – a knowledge deficit with profound implications in terms of identifying and addressing risk factors that contribute to this phenomenon. The present study is one such preliminary attempt to try to fill a significant research and policy gap. Design/methodology/approach The authors draw on data from a sample of 8,261 closed missing person reports from a Canadian municipal police service over a five-year period (2013–2018). Using multiple logistic regression, the authors identify, among other factors, who is most likely to be reported missing from these locations. Findings Results reveal that several factors, such as mental disabilities, senility, mental illness and addiction, are significantly related to this phenomenon. In light of these findings, the authors suggest that there is a need to develop comprehensive strategies and policies involving several stakeholders, such as health care and social service organizations, as well as the police. Originality/value Each year, thousands of people go missing in Canada with a large number being reported from hospitals and mental health units, which can be burdensome for the police and health sectors in terms of human and financial resource allocation. Yet, very little is known about patients reported missing from health services – a knowledge deficit with profound implications in terms of identifying and addressing risk factors that contribute to this phenomenon. This manuscript seeks to remedy this gap in Canadian missing persons literature by exploring who goes missing from hospitals and mental health units.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".