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Record W4401129756 · doi:10.1186/s13104-024-06849-x

A comparison of self-reported chronic disease, health awareness and behaviours in social housing residents: cross-sectional study of communities in Ontario and Quebec

2024· article· en· W4401129756 on OpenAlexafffundabout
Gina Agarwal, Melissa Pirrie, Christie Koester, Drashti Pete, Julia Antolovich, Ricardo Angeles, Francine Marzanek, Magali Girard, Janusz Kaczorowski

Bibliographic record

VenueBMC Research Notes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalMcMaster UniversityCentre Hospitalier de l’Université de MontréalImpactHealth Sciences Centre
FundersCanadian Institutes of Health Research
KeywordsPovertyMedicinePsychological interventionGerontologyCross-sectional studyEnvironmental healthScale (ratio)Public housingSocial supportDemographyGeographyPsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Social housing programs are integral to making housing more affordable to Canadian seniors living in poverty. Although the programs are similar across Canada, there may be inter-provincial differences among the health of residents that could guide the development of interventions. This study explores the health of low-income seniors living in social housing in Quebec and compares it with previously reported data from Ontario. RESULTS: 80 responses were obtained in Quebec to compare with the previously reported Ontario data (n = 599) for a total of 679 responses. More Ontario residents had access to a family doctor (p < 0.001). Quebec residents experienced less problems with self-care (p = 0.017) and less mobility issues (p = 0.052). The visual analog scale for overall health state was similar in both provinces (mean = 67.36 in Ontario and 69.23 in Quebec). Residents in Quebec smoked more cigarettes per day (p = 0.009). More residents in Ontario participated in moderate physical activity (p = 0.09), however, they also spent more time per day on the computer (p = 0.006).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.998

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.388
GPT teacher head0.567
Teacher spread0.180 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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