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Record W7025000974

Supporting Public Health Nurses with Breastfeeding Interventions for Late Preterm Infants

2017· article· en· W7025000974 on OpenAlexaboutno aff

Bibliographic record

VenueMount Royal University Institutional Repository (Mount Royal University) · 2017
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingPsychological interventionPublic healthDelphi methodPostnatal CareBreast feedingPostpartum periodPublic health interventionsHealth care
DOInot available

Abstract

fetched live from OpenAlex

Late preterm infants often experience feeding difficulty post discharge from hospital. While breast milk is especially important for late preterm infants, they have lower exclusive breastfeeding rates than full term infants. This is because mothers of late preterm infants often do not receive sufficient amount of breastfeeding support in the postpartum period. Furthermore, in the Canadian context, guidelines do not exist for health care providers to use to assist them in providing breastfeeding support for mothers of late preterm infants in the community setting. We used a modified Delphi approach to begin to fill this gap. We present information relating to physiological development in systems, its significance to feeding, and potential interventions for public health nurses. This information will assist PHNs in their clinical reasoning and decision-making when supporting mothers and their LPIs to exclusively breastfeed in the community.

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)0.023
metaresearch head score (Gemma)0.055
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 score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.039
GPT teacher head0.298
Teacher spread0.259 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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