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Social determinants of older adults’ awareness of community support services in Hamilton, Ontario

2011· article· en· W1538118760 on OpenAlexafffundabout
Joseph Tindale, Margaret Denton, Jenny Ploeg, Jean Lillie, Brian Hutchison, Kevin Brazil, Noori Akhtar‐Danesh, Jennifer Plenderleith

Bibliographic record

VenueHealth & Social Care in the Community · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityUniversity of Guelph
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsGerontologySocial supportPsychologySociologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Community support services (CSSs) have been developed in Canada and other Western nations to enable persons coping with health or social issues to continue to live in the community. This study addresses the extent to which awareness of CSSs is structured by the social determinants of health. In a telephone interview conducted in February-March 2006, 1152 community-dwelling older adults (response rate 12.4%) from Hamilton, Ontario, Canada were made to read a series of four vignettes and were asked whether they were able to identify a CSS they may turn to in that situation. Across the four vignettes, 40% of participants did name a CSS as a possible source of assistance. Logistic regression was used to determine factors related to awareness of CSSs. Respondents most likely to have awareness of CSS include the middle-aged and higher-income groups. Being knowledgeable about where to look for information about CSSs, having social support and being a member of a club or voluntary organisations are also significant predictors of awareness of CSSs. Study results suggest that efforts be made to improve the level of awareness and access to CSSs among older adults by targeting their social networks as well as their health and social care providers.

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)0.003
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 score0.019
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.123
GPT teacher head0.412
Teacher spread0.289 · 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

Citations10
Published2011
Admission routes3
Has abstractyes

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