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Record W2288980272 · doi:10.1017/s0714980815000586

S<sup>4</sup>AC Case Study: Enhancing Underserved Seniors’ Access to Health Promotion Programs

2016· article· fr· W2288980272 on OpenAlexaff
Sharon Koehn, Sanzida Habib, Syeda Nayab Bukhari

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSimon Fraser UniversityProvidence Health Care
Fundersnot available
KeywordsOutreachFocus groupRecreationImmigrationCandidacyGerontologyAgency (philosophy)NegotiationGeneral partnershipHealth promotionPublic relationsMedicineNursingMedical educationPsychologyPolitical scienceSociologyPublic healthPolitics

Abstract

fetched live from OpenAlex

The Seniors Support Services for South Asian Community (S4AC) project was developed in response to the underutilization of available recreation and seniors' facilities by South Asian seniors who were especially numerous in a suburban neighbourhood in British Columbia. Addressing the problem required the collaboration of the municipality and a registered non-profit agency offering a wide range of services and programs to immigrant and refugee communities. Through creative outreach and accommodation, the project has engaged more than 100 Punjabi-speaking seniors annually in diverse exercise activities. Case study research methods with staff and current and former senior participants of S4AC include participant observation, individual interviews, and focus groups. Viewed through the critical interpretive lens of the "candidacy framework", findings reveal the myriad ways in which access to health promotion and physical activity for immigrant older adults is a complex iterative process of negotiation at multiple levels.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.319
Teacher spread0.266 · 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 designQualitative
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

Citations19
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicHealth disparities and outcomes→French-language works237,207→