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Record W1994714758 · doi:10.1353/cpr.2011.0038

Personal Stories: Voices of Latino Youth Health Advocates in a Diabetes Prevention Initiative

2011· article· en· W1994714758 on OpenAlexaff
Danielle W. Toussaint, Maria Villagrana, Hugo Mora-Torres, Mario de Leon, Mary Hoshiko Haughey

Bibliographic record

VenueProgress in community health partnerships · 2011
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsGeneral partnershipClubGerontologyPublic healthHealth promotionEthnic groupPolitical sciencePsychologyPublic relationsMedicineNursing

Abstract

fetched live from OpenAlex

The YMCA-Silicon Valley Racial and Ethnic Approaches to Community Health (REACH) U.S. Proyecto Movimiento (PM) Action Community project is a community-based partnership that aims to reduce the prevalence of diabetes among Latinos in the Greater Gilroy, California, area by delivering a prevention campaign across generations. A critical component of PM has been the creation of a Youth Health Advocate (YHA) afterschool club at three public high schools in Gilroy. The YHAs, who are trained on health, nutrition, diabetes, basic leadership skills, and digital storytelling, are at the forefront of the campaign targeting Gilroy youth. In their own words, the YHAs describe why they decided to become a YHA, the positive health impact of YHA activities on themselves and their family, and the positive impact on burgeoning leadership skills. The voices of YHAs in this prevention campaigns have brought value to the PM evaluation, and this qualitative element bears further examination in other community-based prevention campaigns.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0180.011
Scholarly communication0.0110.006
Open science0.0020.013
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0030.001

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 head0.452
GPT teacher head0.491
Teacher spread0.039 · 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 designQualitative
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

Citations22
Published2011
Admission routes1
Has abstractyes

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