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Exposure to specific components of a diabetes risk behavior prevention program associated with select psychosocial, dietary and anthropometric outcomes

2008· article· en· W187177714 on OpenAlexaffabout
Joel Gittelsohn, M. Pramod Kumar, Lara S. Ho, Amanda Rosecrans, Rajiv N. Rimal, Stewart B. Harris, Sangita Sharma

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychosocialAnthropometryIntervention (counseling)Body mass indexMedicineEnvironmental healthGerontologyDiabetes mellitusDemographyInternal medicineEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

We implemented a multi‐institutional diabetes risk behavior prevention program in 7 First Nations in northwestern Ontario using culturally relevant community‐, school‐ and store‐based components. Data collected in intervention communities ( n =56 pre‐post respondents) were analyzed to assess the impact of exposure (adjusted for baseline value) on psychosocial outcomes, dietary behaviors and body mass index (BMI). Individual intervention components varied considerably with print media having higher levels of exposure. Exposure to shelf labels had a positive impact on dietary intention (β = 0.217; p=0.017). Dietary intention (β =0.204; p=0.045) and outcome expectations (β =0.224; p=0.019) were also predicted by exposure to the logo. Exposure to flyers was a significant predictor of dietary knowledge (β =0.023; p=0.022) while self‐efficacy was associated with exposure to posters (β =0.0300; p=0.051). Participation in community events was negatively associated with decreased BMI (β = −0.312; p=0.049) after controlling for known predictors. However, overall exposure to the program was not significantly associated with any outcomes, indicating that specific intervention channels are associated with specific outcomes. Intervention programs aimed at changing psychosocial factors and behaviors require greater exposure to more varied intervention components to have a positive impact.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.111
GPT teacher head0.406
Teacher spread0.295 · 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.

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

Citations1
Published2008
Admission routes2
Has abstractyes

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