MétaCan
Menu
Back to cohort
Record W4416926433 · doi:10.36939/cjur/vol32no1/art400

Examining the prevalence of chronic homelessness among single adults according to national definitions in Canada

2023· article· W4416926433 on OpenAlexafffundvenueabout
Ayda Agha, Stephen W. Hwang, Ri Wang, Rosane Nisenbaum, Anita Palepu, Tim Aubry

Bibliographic record

VenueCanadian journal of urban research · 2023
Typearticle
Language
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of OttawaUniversity of British ColumbiaChild Welfare League of Canada
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsMultiple Chronic ConditionsLongitudinal studySample (material)Longitudinal dataChronic diseaseAfrican american

Abstract

fetched live from OpenAlex

This article examines the prevalence of chronic homelessness when applying definitions used in Canada to a sample of homeless and vulnerably housed single adults enrolled in a multi-city longitudinal study. The federal government’s current definition, Reaching Home, identified the highest proportion of homeless single adults (31 percent; 95% CI = 27.2 – 34.1) as “chronically homeless.” Our findings suggest that the federal definitions of chronic homelessness, which are based on both shelter stays and periods of homelessness outside the shelter system, are double the size of this sub-population when compared to definitions based on shelter stays alone. Participants who were male, identified as Indigenous, and reported problematic drug use, were more likely to be chronically homeless for definitions based on any-kind of homelessness. The findings highlight the importance of counting unsheltered and hidden homelessness to estimate the number of single adults who are chronically homeless.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.022
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.271
GPT teacher head0.406
Teacher spread0.135 · 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

Citations0
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueCanadian journal of urban researchSame topicHomelessness and Social IssuesFrench-language works237,207