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Maternal Knowledge of Stunting in Rural Indonesia

2018· article· en· W2900078630 on OpenAlexvenueno aff
Cougar Hall, Cudjoe Bennett, Benjamin T. Crookston, Kirk A. Dearden, Muhamad Hasan, Mary Linehan, Ahmad Syafiq, Scott A. West, Joshua H. West

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Child undernutrition and stunting remain serious public health problems in Indonesia. According to the Health Belief Model, increasing mothers’ knowledge of stunting is fundamental to establishing accurate threat perceptions predictive of behavior change. The purpose of this study was to increase understanding of factors related to maternal knowledge of stunting in Indonesia by addressing three questions: 1) How familiar with stunting are Indonesian mothers? 2) What antecedent factors do Indonesian mothers associate with stunting? and 3) What health effects do Indonesian mothers associate with stunting? A total of 3,150 mothers participated in structured face-to-face interviews. Study measures targeted four main variables. Mothers were asked: 1) Have you heard of stunting?; 2) Have you heard of shortness?; 3) What causes stunting/shortness?; and 4) What are the effects of stunting? Only 66 (2.1%) mothers reported having heard of, read about, or knew something about stunting. Approximately two-thirds of participants attributed stunting to hereditary factors. Interrupted growth (33.7%), idiocy (13.8%), and easy to get sick (11.8%) were identified as health effects of stunting. Results highlight the need for health promotion and education efforts focused on increasing basic knowledge of stunting, its causes, and its health effects among Indonesian mothers

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)0.000
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 score0.073
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.332
Teacher spread0.319 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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