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Record W4213258851 · doi:10.31234/osf.io/cbwj5

Emotional experiences during breastfeeding: time of day, family support and mental health

2022· preprint· en· W4213258851 on OpenAlexaff
Sofia Hempelmann Perez, Sophie Smith, Christina Isaicu, Natasha Binder, Ian Gold, Suparna Choudhury, Radhika Raturi, Charlotte Little, Marie‐Hélène Pennestri, Elizaveta Solomonova

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsHôpital Rivière-des-PrairiesMcGill University
Fundersnot available
KeywordsBreastfeedingMental healthPsychologyContext (archaeology)Emotional supportSleep qualityDevelopmental psychologySocial supportMedicinePsychiatryInsomniaSocial psychologyPediatrics

Abstract

fetched live from OpenAlex

This study aimed to investigate the emotional experience of breastfeeding mothers; to compare their emotions during the day and night; and to identify predictors of maternal emotional states. 107 breastfeeding women completed daytime and nighttime online surveys. Mothers reported a more positive emotional experience during the daytime breastfeeding session. During the day, positive emotional state was most strongly predicted by perceived degree of family’s support, followed by mother’s mental health, overall sleep quality, child’s age, mother’s age and the mother’s immigrant status. In contrast, more positive experience during the nighttime was only associated with better subjectively rated mental health Our results suggest that maternal emotional experience needs to be understood as an interplay between mental health and social context.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.313
Teacher spread0.287 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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