Impact of altered gestational weight gain guidelines on midwives in Japan: A cross-sectional study
Bibliographic record
Abstract
Objective: In March 2021, modifications were made to the dietary guidelines for expectant and nursing mothers in Japan, resulting in an increased recommended gestational weight gain (GWG) based on the pre-pregnancy body mass index. However, the existing landscape of midwives’ health-guidance practices remains unexplored. This study aimed to elucidate the situation and perceptions of the revised GWG guidelines among midwives in Japan.Methods: This cross-sectional study, conducted between January and March 2023, targeted midwives employed across primary, secondary, and tertiary hospitals in Japan. The participants completed a web-based questionnaire via a QR code and provided responses. Descriptive analysis was employed to discern the midwives’ perceptions of the revised GWG guidelines.Results: A total of 160 midwives (24.2%) completed the web-based questionnaire and were included in the analysis. Of them, 117 (73.1%) knew the recommended GWG had been adjusted. A significant difference was observed in the self-evaluation of health guidance before and after the guideline change (p = .015). While 47.9% of the midwives viewed the guideline change positively, 50.4% considered it neither good nor detrimental. The reasons for this positive perspective included the perceived stringency of previous standards and concerns about the potential effects of strict weight guidance on the physical and mental health of both mothers and children. Those with a neutral stance gave the following reasons: 1) uncertainties about the post-change impact and 2) concerns regarding potential health implications for pregnant women gaining excessive weight.Conclusions: Because not all midwives were aware of the guideline adjustments, the new guidelines must be prioritized.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".