A qualitative study exploring factors associated with mothers’ decisions to formula-feed their infants in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
BACKGROUND: Breastfeeding has numerous health benefits. In 2010, the province of Newfoundland and Labrador had the lowest breastfeeding initiation rate (64.0%) in Canada. Formula feeding is associated with well-known health risks. Exclusive formula feeding is the "cultural norm" in some regions of the province. Women appear resistant to changing their infant feeding behaviors and remain committed to their decision to formula-feed. The primary aim of this qualitative study was to examine individual factors that shaped mothers' decisions to formula-feed their infants. Nineteen mothers who were currently formula feeding their children participated in the study. METHODS: Qualitative research in the form of focus groups was conducted in three communities in the province in 2010. A thematic content analysis identified the main themes that influenced mothers' decisions to formula-feed their infants. RESULTS: The main themes included issues concerning the support needed to breastfeed, the convenience associated with formula feeding, and the embarrassment surrounding breastfeeding in public. CONCLUSIONS: These findings help to better understand why mothers choose formula feeding over breastfeeding and may help to inform the development of public health interventions targeted at this population of mothers.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".