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Record W7100979348

Research Khandor et al. Access to primary health care among homeless adults in Toronto, Canada: results from the Street Health survey

2013· article· en· W7100979348 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical Ethics and Theory
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careOdds ratioLogistic regressionOddsPrimary health careConfidence intervalPrimary careHealth equityPublic health
DOInot available

Abstract

fetched live from OpenAlex

Background: Despite experiencing a disproportionate burden of acute and chronic health issues, many homeless people face barriers to primary health care. Most studies on health care access among homeless populations have been conducted in the United States, and relatively few are available from countries such as Canada that have a system of universal health insurance. We investigated access to primary health care among a representative sample of homeless adults in Toronto, Canada. Methods: Homeless adults were recruited from shelter and meal programs in downtown Toronto between November 2006 and February 2007. Cross-sectional data were collected on demographic characteristics, health status, health determinants and access to health care. We used multivariable logistic regression analysis to investigate the association between having a family doctor as the usual source of health care (an indicator of access to primary care) and health status, proof of health insurance, and substance use after adjustment for demographic characteristics. Results: Of the 366 participants included in our study, 156 (43%) reported having a family doctor. After adjustment for potential confounders and covariates, we found that the odds of having a family doctor significantly decreased with every additional year spent homeless in the participant’s lifetime (adjusted odds ratio [OR] 0.91, 95 % confidence interval [CI] 0.86–0.97). Having a family doctor was significantly associated with being lesbian, gay, bisexual or transgendered

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.105
GPT teacher head0.365
Teacher spread0.259 · 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.

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
Published2013
Admission routes1
Has abstractyes

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