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Record W3038953649 · doi:10.1177/2333794x20924505

Exploring Knowledge and Perspectives of South Asian Children and Their Parents Regarding Healthy Cardiovascular Behaviors: A Qualitative Analysis

2020· article· en· W3038953649 on OpenAlexaff
Kaitey Vincent, Zubin Punthakee, Charlotte Waddell, Miriam P. Rosin, Navjot Sran, Scott A. Lear

Bibliographic record

VenueGlobal Pediatric Health · 2020
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of British ColumbiaProvidence Health CareMcMaster UniversityPopulation Health Research InstituteSimon Fraser University
Fundersnot available
KeywordsMedicinePsychological interventionDiseaseBody mass indexDevelopmental psychologySouth asiaPopulationGerontologyEnvironmental healthPsychologyPsychiatryPathology

Abstract

fetched live from OpenAlex

South Asian children and parents have been shown to have a higher risk for cardiovascular disease (CVD) relative to white individuals. To design interventions aimed at addressing the comparatively higher burden in South Asians, a better understanding of attitudes and perspectives regarding CVD-associated behaviors is needed. As a result, we sought to understand knowledge about CVD risk in both children and parents, and attitudes toward physical activity and diet in both the children and parents, including potential cultural influences. In-depth interviews were conducted with 13 South Asian child-and-parent dyads representing a range of child body mass index (BMI) levels, ages, and with both sexes. South Asian children and parents demonstrated good knowledge about CVD prevention; however, knowledge did not always translate into behavior. The influence of social and cultural dynamics on behavior was also highlighted. To ensure that interventions aimed at this population are effective, an understanding of the unique social dynamics that influence diet and physical activity-related behaviors is needed.

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)0.001
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 score0.067
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.082
GPT teacher head0.348
Teacher spread0.265 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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