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Record W4405337365 · doi:10.1177/26320770241275094

Prenatal Breastfeeding Education: A Health Equity Approach

2024· article· en· W4405337365 on OpenAlexaff
Hermandeep Deo, Helen Brown

Bibliographic record

VenueJournal of Prevention and Health Promotion · 2024
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBreastfeedingHealth equityEquity (law)Health promotionNursingBreastfeeding promotionHealth educationPsychologyMedicineEconomic growthPublic relationsPolitical sciencePublic healthPediatricsEconomics

Abstract

fetched live from OpenAlex

Breastfeeding has a direct impact on the health of individuals and communities. Despite the implementation of diverse health promotion and education strategies, global breastfeeding rates remain relatively low. Predominantly, these strategies are implemented during the prenatal period and focus on individual-level education and behavior change, with less attention paid to the structural and contextual resources necessary for breastfeeding support and education. Breastfeeding experiences are contextually shaped by social, cultural, economic, and political factors. In addition, breastfeeding education, support, and rates are influenced by complex structures such as policy. Thus, a health equity approach to guide the planning and implementation of prenatal breastfeeding education can contribute to improving breastfeeding rates for diverse individuals and populations. Drawing from critical theoretical and health equity concepts, we present an equity approach with the potential for direct application to educating parents about breastfeeding. We discuss how a health equity approach could guide policymakers and perinatal healthcare providers to ensure culturally, emotionally, and psychologically safe learning spaces to reduce access barriers to breastfeeding education. Also, the approach could tailor the learning of parents to accommodate the unique contexts of their lives, thereby optimizing their breastfeeding experiences and outcomes.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.350

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 communication0.0000.001
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 head0.115
GPT teacher head0.462
Teacher spread0.347 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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