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Victimization, safety, and overdose in homeless shelters: A systematic review and narrative synthesis

2023· review· en· W4385326982 on OpenAlexafffund
Nick Kerman, Sean A. Kidd, Joseph Voronov, Carrie Anne Marshall, Branagh R. O’Shaughnessy, Alex Abramovich, Vicky Stergiopoulos

Bibliographic record

VenueHealth & Place · 2023
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsPublic Health OntarioUniversity of TorontoWestern UniversityCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsHarmPsychological interventionInterpersonal violenceSuicide preventionOccupational safety and healthPoison controlInjury preventionMedicineEnvironmental healthHarm reductionHuman factors and ergonomicsInterpersonal communicationPsychologyPsychiatryNursingPublic healthSocial psychology

Abstract

fetched live from OpenAlex

The objective of this prospectively registered systematic review was to identify the factors that contribute to sense of safety, victimization, and overdose risk in homeless shelters, as well as groups that are at greater risk of shelter-based victimization. Fifty-five articles were included in the review. Findings demonstrated that fears of violence and other forms of harm were prominent concerns for people experiencing homelessness when accessing shelters. Service users' perceptions of shelter dangerousness were shaped by the service model and environment, interpersonal relationships and interactions in shelter, availability of drugs, and previous living arrangements. 2SLGBTQ+ individuals were identified as being at heightened risk of victimization in shelters. No studies examined rates of shelter-based victimization or tested interventions to improve safety, with the exception of overdose risk. These knowledge gaps hinder the establishment of evidence-based practices for promoting safety and preventing violence in shelter settings.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.469
Teacher spread0.387 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations28
Published2023
Admission routes2
Has abstractyes

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