Profiling persons reported missing from hospitals versus mental health facilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it