Priorities for Contraception and Lactation Among Breast Pump-Dependent Mothers of Premature Infants in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".