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Record W2313569967 · doi:10.1097/fch.0b013e3181c4e252

Primary Prevention of Lead Poisoning in Rural Native American Children

2010· article· en· W2313569967 on OpenAlexaff
Michelle C. Kegler, Lorraine Halinka Malcoe, Veronika Fedirko

Bibliographic record

VenueFamily & Community Health · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsSimon Fraser University
FundersNational Institute of Environmental Health Sciences
KeywordsNative americanLead poisoningSuperfundEnvironmental healthMississippi deltaMedicinePrimary preventionPopulationAfrican americanWhite (mutation)Rural communityIntervention (counseling)GerontologyDemographyNursingPsychiatryHazardous wasteEngineering

Abstract

fetched live from OpenAlex

The current study examined the effectiveness of a community-based lay health advisor intervention, combined with youth engagement, in improving lead poisoning prevention behaviors and associated beliefs in a rural Native American population located in and near a Superfund site containing mining waste. Three sequential (1997, 2000, and 2004) cross-sectional assessments involving in-person interviews with Native American and White caregivers of young children were conducted. Results showed significant improvements over time for Native American, but not for White, for children washing their hands before meals and snacks, and for annual blood lead testing of both Native American and White children. Findings lend support to the value of community-based education for primary prevention of lead poisoning in Native American and rural 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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.299
Teacher spread0.281 · 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 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

Citations10
Published2010
Admission routes1
Has abstractyes

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