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Record W3188929011 · doi:10.1177/10105395211038458

Cultural Determinants of Fruits and Vegetable Consumption in Indigenous ( <i>iTaukei</i> ) Fijian Children: A Qualitative Study of Caregivers

2021· article· en· W3188929011 on OpenAlexaff
Salanieta M. C. Hawea, Pragya Singh, Susan J. Whiting

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsThematic analysisIndigenousConsumption (sociology)Focus groupQualitative researchCulturally appropriatePerceptionPopulationFish <Actinopterygii>Healthy foodEnvironmental healthGerontologyPsychologyMedicineFood scienceSociologySocial scienceEcologyAnthropology

Abstract

fetched live from OpenAlex

The consumption of fruits and vegetables (F&V) has many health benefits, yet the majority of the world's population, including young children, consume less than recommended. This article provides caregivers' perspectives on cultural determinants of F&V consumption in children in Fiji. A qualitative study design using focus group discussions with caregivers of children aged 6 months to 5 years old in Suva was used. Thematic content analysis was undertaken to identify common issues using four main themes. Subthemes were further analyzed from the broad themes to understand caregiver's perceptions. Caregivers perceived that meal components lacked F&V and food preparation and cooking methods of F&V did not stimulate children's appetite. Non-vegetable products such as meat and fish were perceived as more valued and privileged for those consuming them. Understanding cultural determinants as perceived by caregivers is important to inform strategies to increase F&V consumption in children.

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)0.002
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 score0.004
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.365
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

Citations9
Published2021
Admission routes1
Has abstractyes

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