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Record W2890445251 · doi:10.1080/10530789.2018.1523103

Cold-related injuries in a cohort of homeless adults

2018· article· en· W2890445251 on OpenAlexafffundabout
Paige Zhang, Kate Bassil, Stephanie Gower, Marko Katić, Alex Kiss, Evie Gogosis, Stephen W. Hwang

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoInstitute for Clinical Evaluative SciencesToronto Public HealthSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchAgency for Healthcare Research and Quality
KeywordsMedicineEmergency departmentPsychological interventionLow incomeGerontologyDemographyPublic healthCohortPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

People experiencing homelessness have an increased risk of cold-related injuries. This study determined the rate of emergency department (ED) visits for cold-related injuries among homeless adults and low-income controls in Toronto, Canada. Homeless individuals were recruited at shelters and meal programs. Age- and sex-matched controls living in low-income neighborhoods were selected. ED utilization was ascertained over 4-years of follow-up (2005–9) using administrative databases. A total of 16 ED visits for cold-related injuries were observed among 587 homeless men and 296 homeless women. The rate of ED visits was 6.7 (95% CI, 4.2–12.4) per 1000 person-years of observation among homeless men and 0.9 (95% CI, 0.03–5.6) among homeless women. ED visit rates were significantly higher among homeless men compared to low-income men (P < 0.001) and significantly higher among homeless men compared to homeless women (P = 0.03). Targeted public health interventions are needed to reduce the risk of cold-related injuries among people experiencing homelessness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

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

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 head0.013
GPT teacher head0.344
Teacher spread0.332 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2018
Admission routes3
Has abstractyes

Explore more

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