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Record W2090590684 · doi:10.1177/1524839905278626

Effective Components for Nutrition Interventions: A Review and Application of the Literature

2006· review· en· W2090590684 on OpenAlexaffabout
Tina B. Sahay, Fredrick D. Ashbury, Melody Roberts, Irving Rootman

Bibliographic record

VenueHealth Promotion Practice · 2006
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsMichael Smith Health Research BCUniversity of VictoriaCancer Care OntarioUniversity of Toronto
Fundersnot available
KeywordsIntervention (counseling)Psychological interventionSocial cognitive theoryMedicineIntervention mappingNursingGerontologyPsychologyFamily medicineHealth promotionPublic healthDevelopmental psychology

Abstract

fetched live from OpenAlex

A review of the nutrition intervention literature was conducted for Cancer Care Ontario (CCO) to develop a provincial nutrition and healthy body weight strategy. Controlled trials that were conducted between 1994 and 2000 in North America, Europe, Australia, and New Zealand were included. Fifteen interventions were included, 10 of which showed significant intervention effect and 5 reporting negative effect. Elements of effective interventions included theoretical basis, family involvement, participatory planning and implementation models, clear messages, and adequate training and ongoing support for intervenors. CCO applied these practices to design a pilot intervention. Stakeholders participated in the intervention design and tested for clear messaging. Consistent with social cognitive theory, the intervention included activities for children and parents and provided environmental supports such as transportation and child care. Training and support for implementers and evaluators was provided by CCO.

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)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0200.016
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.166
GPT teacher head0.577
Teacher spread0.411 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations65
Published2006
Admission routes2
Has abstractyes

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