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Record W4367835418 · doi:10.1186/s13643-023-02239-9

Breastfeeding support provided by lactation consultants in high-income countries for improved breastfeeding rates, self-efficacy, and infant growth: a systematic review and meta-analysis protocol

2023· review· en· W4367835418 on OpenAlexafffund
Curtis J. D’Hollander, Victoria McCredie, Elizabeth Uleryk, Charles Keown‐Stoneman, Catherine S. Birken, Deborah L. O’Connor, Jonathon L. Maguire

Bibliographic record

VenueSystematic Reviews · 2023
Typereview
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsInstitute for Clinical Evaluative SciencesInstitute for Work & HealthToronto Western HospitalCanadian Nutrition SocietyUniversity of TorontoUniversity Health NetworkHospital for Sick ChildrenPublic Health OntarioSt. Michael's Hospital
FundersInstitute of Human Development, Child and Youth Health
KeywordsBreastfeedingMedicinePsychological interventionCINAHLScopusMeta-analysisMEDLINEBreast feedingFamily medicineSystematic reviewNursingPediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: It is well established that breast milk offers numerous health benefits for mother and child. Mothers are recommended to exclusively breastfeed their child until 6 months of age, with continued breastfeeding up to 1-2 years of age or beyond. Yet, these recommendations are met less than half of the time in high-income countries. Lactation consultants specialize in supporting mothers with breastfeeding and are a promising approach to improving breastfeeding rates. For lactation consultant interventions to be implemented widely as part of public health policy, a better understanding of their effect on breastfeeding rates and important health outcomes is needed. METHODS: The overall aim of this systematic review is to evaluate the effect of lactation consultant interventions provided to women, compared to usual care, on breastfeeding rates (primary outcome), maternal breastfeeding self-efficacy, and infant growth. A search strategy has been developed to identify randomized controlled trials published in any language between 1985 and April 2023 in CENTRAL, MEDLINE, EMBASE, CINAHL, Scopus, and Web of Science. We will also perform a search of the grey literature and reference lists of relevant studies and reviews. Two reviewers will independently extract data on study design, baseline characteristics, details of the interventions employed, and primary and secondary outcomes using a pre-piloted standardized data extraction form. Risk of bias and quality of evidence assessment will be done independently and in duplicate using the Cochrane Risk of Bias tool and GRADE approach, respectively. Where possible, meta-analysis using random-effects models will be performed, otherwise a qualitative summary will be provided. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. DISCUSSION: This review will fill an important gap in the lactation support literature. The findings will be of importance to policymakers who seek to implement interventions to improve breastfeeding rates. TRIAL REGISTRATION: This review has been registered in the PROSPERO database (ID: CRD42022326597).

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)0.044
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.044
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.061
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0290.031
Bibliometrics0.0150.012
Science and technology studies0.0030.003
Scholarly communication0.0070.006
Open science0.0050.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0350.003

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 head0.078
GPT teacher head0.398
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreProtocol

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
Published2023
Admission routes2
Has abstractyes

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