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Record W2079535436 · doi:10.3148/65.3.2004.101

<i>Fruit and Vegetable Consumption:</i>Benefits and Barriers

2004· article· en· W2079535436 on OpenAlexaffvenue
Debbie MacLellan, Katherine Gottschall‐Pass, Roberta Larsen

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2004
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsThematic analysisCredibilityConsumption (sociology)Context (archaeology)Environmental healthPsychological interventionQualitative researchFood choiceGerontologyMedicinePsychologyNursingGeographySociologyPolitical science

Abstract

fetched live from OpenAlex

Few people on Prince Edward Island meet the goal of consuming five or more servings of vegetables and fruit a day. The main objective of this qualitative study was to explore the perceptions of the nutritional benefits and barriers to vegetable and fruit intake among adult women in Prince Edward Island. Participants were 40 women aged 20-49, with or without children at home, who were or were not currently meeting the objective of eating five or more fruit and vegetable servings a day. In-home, one-on-one interviews were used for data collection. Thematic analysis was conducted on the transcribed interviews. Data were examined for trustworthiness in the context of credibility, transferability, and dependability. Most participants identified one or more benefits of eating fruit and vegetables; however, comments tended to be non-specific. The main barriers that participants identified were effort, lack of knowledge, sociopsychological and socioenvironmental factors, and availability. Internal influences, life events, and food rules were identified as encouraging women to include vegetables and fruit in their diets. Given the challenges of effecting meaningful dietary change, dietitians must look for broader dietary behavioural interventions that are sensitive to women's perceptions of benefits and barriers to fruit and vegetable intake.

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)0.003
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 score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.358
Teacher spread0.294 · 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

Citations41
Published2004
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Dietetic Practice and ResearchSame topicObesity, Physical Activity, DietFrench-language works237,207