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Record W4405892129 · doi:10.1089/chi.2024.0374

Differences in Geographical Location and Health Behaviors of Participants in a Family-Based Lifestyle Intervention for Children and Adolescents Living with Obesity

2024· article· en· W4405892129 on OpenAlexaffabout
Alexandra J. Heidl, Madelaine Gierc, Stephanie Saputra, Thumri Waliwitiya, Eli Puterman, Tamara R. Cohen

Bibliographic record

VenueChildhood Obesity · 2024
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsConcordia UniversityBC Children's HospitalUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsIntervention (counseling)ObesityGerontologyPsychologyChildhood obesityHealth behaviorMedicineDevelopmental psychologyEnvironmental healthOverweightPsychiatry

Abstract

fetched live from OpenAlex

It is unknown if children and youth who live in rural or “less rural” locations who enroll in the provincially funded Generation Health Clinic (British Columbia, Canada), a family-based lifestyle program for weight management, present with different health behaviors at baseline. Thus, we assessed sociodemographic and health behavior (diet, physical activity, and sleep) collected between 2015 and 2019. Data were stratified by age (children: ≤12 years; adolescents: ≥13 years) and geographical location (“less urban” and urban) based on Statistics Canada definitions and then analyzed using independent t-tests and chi-square tests. We found that more “urban” children consumed more daily family meals (p < 0.001), ate out weekly (p = 0.02), ate “other” vegetables (p = 0.002), and had less frequent sports drink consumption (p < 0.001) compared with less urban children. No significant differences in health behaviors were seen in adolescents. These findings suggest that a participant’s geographical location should be considered when developing family-based interventions for weight management.

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 score0.005
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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