Midwives’ knowledge and health guidance practices regarding gestational weight gain in Mongolia: A cross-sectional study
Bibliographic record
Abstract
Objective: Excessive gestational weight gain (GWG) affects perinatal outcomes. However, to our knowledge, there are no studies on midwives’ knowledge and health guidance practices regarding GWG in Mongolia. Therefore, this study aimed to investigate midwives’ knowledge about weight control, clarify GWG guidance during pregnancy, and identify factors related to the implementation of GWG guidance in Mongolia. Methods: A cross-sectional study using a web-based questionnaire was conducted among midwives registered with the Mongolian Midwives Association. The survey was conducted between July and August 2024. The questionnaire asked about calculating body mass index (BMI), BMI categories, and implementation of GWG health counseling. Multiple logistic regression analyses were conducted to identify factors associated with health guidance on GWG and BMI knowledge. Results: A total of 414 responses were analyzed. Of the participants, 38.4% could correctly calculate BMI, and 37.7% could correctly answer what the BMI “normal weight” category was. Furthermore, 38.6% to 77.8% of midwives provided the 10 recommended health guidance items to more than 90% of pregnant women. Midwives working in hospital wards, those with fewer years of clinical experience, those who did not know how to calculate BMI, and those who did not have experience learning about weight control were associated with lower GWG health guidance scores. Conclusions: This study clarified the health guidance practices regarding GWG and determined midwives’ level of knowledge about weight control in Mongolia. Postgraduate education for midwives should be enhanced to improve health guidance for GWG in Mongolia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".