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Record W3124700071

Where Would You Turn For Help? Older Adults’ Knowledge and Awareness of Community Support Services

2009· article· en· W3124700071 on OpenAlexfundno aff
Margaret Denton, Jenny Ploeg, Joseph Tindale, Brian Hutchison, Kevin Brazil, Noori Akhtar‐Danesh, Monica Quinlan

Bibliographic record

VenueEconstor (Econstor) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsReferralAcquiescenceService providerWord of mouthPsychologyCoping (psychology)Public relationsService (business)MedicineNursingBusinessMarketingPsychiatryPolitical sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

Community support services (CSSs) enable persons coping with health or social problems to maintain the highest possible level of social functioning and quality of life. Access to these services is challenging because of the multiplicity of small agencies providing these services and the lack of a central access point. A review of the literature revealed that most service awareness studies are marred by acquiescence bias. To address this issue, service providers developed a series of 12 vignettes to describe common situations faced by older adults for which CSSs might be appropriate. In a telephone interview, 1152 older adults were presented with a series of vignettes and asked what they would do in that situation. They were also asked about their most important sources of information about CSSs. Findings show awareness of CSSs varied by the situation described and ranged from a low of 1% to 41%. The most important sources of information about CSSs included informational and referral sources, the telephone book, doctor's offices, and through word of mouth.

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)0.015
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.325
Teacher spread0.301 · 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
Published2009
Admission routes1
Has abstractyes

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