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Record W2607575951 · doi:10.14391/ajhs.12.46

Encouraging Exercise Participation amongst UK South Asians

2017· article· en· W2607575951 on OpenAlexaboutno aff
Nigel King, A.H. Little

Bibliographic record

VenueAsian Journal of Human Services · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionHealth promotionPopulationGerontologyQualitative researchPromotion (chess)PsychologyObesityMedicinePublic healthPolitical scienceEnvironmental healthSociologyNursingSocial science

Abstract

fetched live from OpenAlex

Regular physical activity(PA) is recognised as playing a key role in promoting good health and tackling obesity. In many parts of the world there are concerns that people do not undertake sufficient PA, and that this problem is often worse for certain groups in the population. Low levels of PA amongst South Asian (SA) adults in the United Kingdom concern health policy makers and professionals because of the higher incidence of heart disease in this group than in the general population. Interventions have helped increase PA levels in white populations but have shown little success in engaging SA adults. One explanation is that interventions emphasise individual responsibility for health and pay relatively less attention to socio-cultural constraints on behaviour. Using qualitative, semi-structured interviews, we investigated influences on PA amongst 13 SA adults (aged 23-70) living in Halifax, Yorkshire, UK. The setting for our study was the participants’ community gym. A key aim was to identify characteristics of the gym that influenced usage by the local SA population. We found the gym had successfully engaged SA adults in a programme of regular PA, and that a sense of its “embeddedness” in the local community was crucial to this. Implications for practice and research in health promotion and obesity prevention are discussed.

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)0.000
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 score0.261
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.354
Teacher spread0.312 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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