Mothers' Experiences with Baby Scales in the First Two Weeks Post Birth: A Qualitative Study
Bibliographic record
Abstract
Introduction: Health care professionals are divided on the topic of routine weight measurements for healthy breastfed newborns. This study presents interviews with a subset of participants from a larger study. The interviews provided an opportunity to look at weighing babies from a different perspective, specifically, when mothers are routinely weighing their babies and when they have use and control of a baby scale. Objective: To describe women's experiences using baby scales and weighing their babies daily in their own homes during the first two weeks postpartum. Methods: Qualitative descriptive design comprised of telephone interviews in a mid-sized Canadian city. Eight participants were from a larger study about newborn weight loss. Results: The overall theme to emerge from the data was “the baby scale as a tool” and five subthemes emerged: builds confidence; fosters reassurance; offers convenience; provides information; and satisfies curiosity. Conclusions: This study produced new information about how breastfeeding mothers felt about using baby scales in their own homes. Contrary to what some might assume, weighing babies did not cause mothers distress and worry; it usually provided reassurance. Any discussion about regularly weighing healthy newborns assumed clinicians would weigh the babies. Giving women control of the baby scale might affect breastfeeding outcomes.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".