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Record W4225582279 · doi:10.1177/08445621221089475

Birth Experiences, Breastfeeding, and the Mother-Child Relationship: Evidence from a Large Sample of Mothers

2022· article· en· W4225582279 on OpenAlexvenueno aff
Abi M. B. Davis, Valentina Sclafani

Bibliographic record

VenueCanadian Journal of Nursing Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingMedicineDevelopmental psychologyCross-sectional studyAffect (linguistics)PsychologyFamily medicineDemographyNursingPediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: It is a priority for public health professionals to improve global breastfeeding rates, which have remained low in Western countries for more than a decade. Few researchers have addressed how maternal perceptions of birth experiences affect infant feeding methods. Furthermore, mixed results have been shown in research regarding breastfeeding and mother-child bonding, and many studies are limited by small sample sizes, representing a need for further investigation. PURPOSE: We aimed to examine the relationship between subjective birth experiences and breastfeeding outcomes, and explored whether breastfeeding affected mother-infant bonding. METHODS: 3,080 mothers up to three years postpartum completed a cross - sectional survey. RESULTS: Mothers who had more positive birth experiences were more likely to report breastfeeding their babies. Moreover, mothers who perceived their birth as more positive were more likely to breastfeed their child for a longer period (over 9 months) than those who had more negative experiences. In line with recent research, breastfeeding behaviours were not associated with reported mother-infant bonding. CONCLUSIONS: Mothers who reported better birth experiences were most likely to breastfeed, and breastfeed for longer. We find no evidence to suggest that feeding methods are associated with bonding 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)0.004
metaresearch head score (Gemma)0.003
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 score0.103
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.388
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 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

Citations46
Published2022
Admission routes1
Has abstractyes

Explore more

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