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Record W2064402194 · doi:10.3402/ijch.v65i1.17892

Community-based communication strategies to promote infant iron nutrition in northern Canada

2006· article· en· W2064402194 on OpenAlexafffundabout
Tanya Verrall, Lily Napash, Lucie Leclerc, Sophie Mercure, Katherine Gray‐Donald

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2006
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsCree Board of Health and Social Services of James BayMcGill University
FundersCanadian Institutes of Health ResearchHealth CanadaCree Board of Health and Social Services of James Bay
KeywordsIntervention (counseling)Environmental healthMedicineSocial marketingBaby foodPsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate innovative communication strategies promoting iron nutrition for infants at risk for iron deficiency anemia (IDA) in a northern Aboriginal community. STUDY DESIGN: A prospective process evaluation. METHODS: A social marketing approach was used in the development, implementation and evaluation of the communication strategies. A post-intervention questionnaire was administered to a sample (n = 45) to evaluate reach and exposure of the strategies, and sales of iron-rich infant foods were examined pre- and post-intervention. RESULTS: Multiple communication channels were associated with an increased awareness of IDA and an increased self-reported use of iron-rich infant food. Radio was the most successful channel for reach and exposure of messages. Iron-rich infant food sales increased from pre- to post-intervention (p < 0.05). Breadth of exposure to cooking activity was more limited; however, participants reported increased confidence in preparing homemade baby food. CONCLUSIONS: Communication strategies are a promising strategy for infant IDA prevention where appropriate food is available.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.301
Teacher spread0.288 · 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

Citations14
Published2006
Admission routes3
Has abstractyes

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