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Record W4200086311 · doi:10.21428/cb6ab371.b010d2c5

How Far Do They Go?: A Spatial Examination of Missing Persons from Hospitals

2021· preprint· en· W4200086311 on OpenAlexaff
Lorna Ferguson, Jacek Koziarski

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsMissing dataVolition (linguistics)HarmPerspective (graphical)Harm reductionSample (material)MedicineService (business)PsychologyGeographyNursingBusinessSocial psychologyComputer sciencePublic health

Abstract

fetched live from OpenAlex

Missing person cases are a global issue impacting policing. Among these, those who abscond from hospitals are especially concerning because these reports require collaboration across services, often strain already limited police and hospital resources, and present an elevated level of possible harm due to the high prevalence of mental illness, disability, and/or addiction. Despite this, to date, there has been a lack of scholarly attention on this phenomenon from a policing perspective. The present study aims to fill this gap by exploring how far missing hospital patients travel and where they are commonly found. Using a sample of 731 closed case files (2014-2018) from one police service, we identify spatial behaviour patterns specific to this group of missing persons. Results suggest that most do not leave the hospital grounds or stay within a 5-kilometer radius. Others were found close to the hospital and within city limits and returned of their own volition. By identifying these spatial behaviour patterns associated with missing hospital patients, police can refine probable search areas, allocate resources more efficiently, find the missing faster, and develop better-informed responses and policies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.054
GPT teacher head0.338
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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