Obstetric healthcare providers’ perceptions of communicating gestational weight gain recommendations to overweight/obese pregnant women
Bibliographic record
Abstract
BACKGROUND: Gestational weight gain (GWG) is a major risk factor of poor pregnancy outcomes. Obese pregnant women frequently report bias and discrimination when dealing with healthcare providers (HCPs). Effective communication of GWG recommendations may impact risks. Study objectives were to identify perceptions of HCPs in communicating GWG recommendations and to identify potential gaps/opportunities that could be addressed in the development of appropriate materials/programmes. METHODS: A survey tool was created using the Theory of Planned Behaviour to capture HCPs' attitudes, behaviours and intentions, using four-point Likert questions. Surveys were distributed to obstetricians/gynaecologists, family physicians, obstetric residents/ fellows, midwives, registered/public health nurses and registered dietitians. RESULTS: Results from 96 surveys show that HCPs agreed discussing GWG was important (100%), beneficial for patient-provider rapport (86%) and best practice (100%); however, most found it unpleasant (68%). Providers have confidence in their skills to provide nutrition advice (71%) and believe they have sufficient training (56%); yet, 31% acknowledged making derogatory comments and indicated that they could improve their communication of GWG (92%). CONCLUSIONS: HCPs believe they are providing GWG recommendations in an effective and empathetic manner. While an underlying current of bias/discrimination remains, there is recognition of the importance of more training and access to appropriate tools.
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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 | 0.049 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".