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Record W2061839102 · doi:10.3138/ptc.2012-70bc

Ischaemic Heart Disease–Related Knowledge, Behaviours, and Beliefs of Indo-Canadians and Euro-Canadians: Implications for Physical Therapists

2014· article· en· W2061839102 on OpenAlexaffvenueabout
Giselle Rodrigues, Lyn Jongbloed, Zhenyi Li, Elizabeth Dean

Bibliographic record

VenuePhysiotherapy Canada · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British ColumbiaRoyal Roads University
Fundersnot available
KeywordsCollectivismIschaemic heart diseaseGerontologyMedicinePsychologyImmigrationPolitical scienceIndividualism

Abstract

fetched live from OpenAlex

PURPOSE: To examine knowledge, behaviours, and beliefs related to ischaemic heart disease (IHD) of Indo-Canadians (ICs), thereby helping target health education strategies. METHODS: In a cross-sectional descriptive/comparative study, 102 Indian-born Indo-Canadians (ICs) and 102 Canadian-born Euro-Canadians (ECs) completed a standardized questionnaire on IHD knowledge and lifestyle-related behaviours and beliefs. RESULTS: Compared with ECs, ICs were less aware of IHD-risk factors. ICs' lifestyle practices and beliefs were consistent with having less perceived control over health than ECs. ICs reported more stress from various sources and resorted less to exercise for stress relief and more to religious/spiritual activities. CONCLUSIONS: In accordance with health belief theory, approaches to educating immigrants from collectivistic cultures such as India to assume responsibility for their personal health may need to be different from those used with ECs, which stress self-management. Such programmes may need to emphasize lifestyle-related health knowledge and beliefs as bases for health behaviour change.

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 score0.017
Threshold uncertainty score0.073

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 studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.313
Teacher spread0.302 · 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

Citations15
Published2014
Admission routes3
Has abstractyes

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