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Record W2946715059 · doi:10.1089/bfm.2019.0007

Priorities for Contraception and Lactation Among Breast Pump-Dependent Mothers of Premature Infants in the Neonatal Intensive Care Unit

2019· article· en· W2946715059 on OpenAlexaff
Beverly Rossman, Ifeyinwa V. Asiodu, Rebecca Hoban, Aloka L. Patel, Janet L. Engstrom, Clarissa Medina-Poeliniz, Paula P. Meier

Bibliographic record

VenueBreastfeeding Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineBreastfeedingNeonatal intensive care unitLactationObstetricsBreast feedingBreast milkPediatricsPregnancy

Abstract

fetched live from OpenAlex

Objective: Determine the knowledge and priorities for postpartum contraception and lactation in mothers of premature infants. Design: Twenty-five mothers of premature infants (mean gestational age = 29.9 weeks) hospitalized in a tertiary neonatal intensive care unit (NICU) participated in a multi-methods study using a multiple-choice contraceptive survey and qualitative interview in the first 2 weeks postpartum. Data were analyzed using content analysis and descriptive statistics. Results: Although 60% of mothers planned to use contraception, all questioned the timing of postpartum contraceptive counseling while recovering from a traumatic birth and coping with the critical health status of the infant. All mothers prioritized providing mothers' own milk (MOM) over the use of early hormonal contraception because they did not want to “take any risks” with their milk. They had limited knowledge of risks for repeat preterm birth (e.g., prior preterm birth: n = 13, 52%; multiple birth: n = 9, 36%; no knowledge: n = 3, 12%); only two mothers (0.08%) were counseled about the risks of a short interpregnancy interval. Conclusion: The context of the infants' NICU admission and the mother's desire to “do what is best for the baby” by prioritizing MOM should be integrated into postpartum contraceptive counseling for this population.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.289
Teacher spread0.272 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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