Profiling persons reported missing from hospitals versus mental health facilities
Bibliographic record
Abstract
Missing person reports from hospitals and mental health facilities are a significant issue impacting patients, communities, and health and police sectors. Research on missing persons seldom considers the type of location from where people go missing, which can be troublesome due to the increased chances for experiencing harm during an episode from hospitals and mental health facilities. When location type is studied, these often remarkably different places are frequently blended together in analyses and discussions. This conflation has implications for research and the development of effective police preventive responses. To begin to address this gap, this study uses descriptive analysis and logistic regression to examine the descriptive and predictive profiles of those reported missing from hospitals versus those reported missing from mental health units. For this, data are taken from a sample of 916 closed missing person cases reported to a Canadian municipal police service over five years. Results suggest there are significant differences in both the descriptive and predictive profiles of individuals reported missing from these two location types, such as individuals with varying mental health and cognitive issues going missing from each place, respectively. Given the findings, the implications for research, policing, and risk management are discussed.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".