Gestational Weight Monitoring in Rural and Regional Populations: Women's Knowledge, Experience and Recommendations for Models of Care
Bibliographic record
Abstract
OBJECTIVE: To explore women's knowledge and experience of weight monitoring during pregnancy to inform the development of a model of care that meets demonstrated needs. SETTING: A rural and regional health service in southern Queensland. PARTICIPANTS: Women (n = 160) who used antenatal care in the health service from June 2018 to October 2022. DESIGN: An exploratory online survey was sent via short messaging service to women, including quantitative and qualitative questions with free-text options for additional comments. The data were analysed using descriptive statistics. RESULTS: One in five women could correctly identify the recommended gestational weight gain based on their pre-pregnancy body mass index. Half the women reported knowing weight gain recommendations was useful. A quarter of women had a negative experience with health professionals discussing their weight. One-fifth of women saw a dietitian, and an additional 9% would have liked to use the service, with 14% not knowing it was available. CONCLUSION: Women would like to know more about achieving healthy weight gain and receive support to do so. Women report experiencing stigma when discussing pregnancy weight. Whilst the findings are similar to urban women's experience, rural women's ability to access care in the context of a rural setting presents a unique set of barriers. Further investigation is required to gather health professionals' experience in conjunction with the latest evidence to inform improvements to service delivery.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".