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Using the Community-Based Participatory Research (CBPR) Approach in Childhood Obesity Prevention

2014· article· en· W2124470654 on OpenAlexvenueno aff
Janavi Kumar, Tandalayo Kidd, Yijing Li, Erika Lindshield, Nancy Muturi, Koushik Adhikari

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsMedicineCommunity-based participatory researchChildhood obesityParticipatory action researchEnvironmental healthObesityGerontologyEconomic growth

Abstract

fetched live from OpenAlex

Childhood and adolescent obesity has increased drastically in the past 30 years. While this is troubling, there is also evidence of large disparities among certain ethnic groups such as African American and Hispanic children and adolescents. The Community-Based Participatory Research (CBPR) Model emphasizes a collaborative, co-learning, mutually beneficial, and community-partnered approach to research. Unique aspects of this model include viewing community members as equal partners in non-hierarchical teams, working together in a strengths-based, action oriented research process. This review consists of an investigation of the CBPR approach, its important tenets, and why such an approach may be more effective for childhood and adolescent obesity intervention program development, especially in stratified communities.

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)0.003
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 score0.183
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.162
GPT teacher head0.433
Teacher spread0.271 · 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
Published2014
Admission routes1
Has abstractyes

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