P05.65. BHIP - be healthy in pregnancy: strategies nutritional and physical activity interventions to improve gestational weight gain management
Bibliographic record
Abstract
This project uses a qualitative approach employing focus groups and interviews of participant women (pregnant or recently pregnant) and health care providers that aims to identify the preferred evidence-based strategies for women to effectively manage their GWG during and after pregnancy and how best to implement the selected intervention. Primary research question: What are the preferences of pregnant and post-partum women and their health providers for engaging in healthy eating and increased physical activity? Secondary questions include: What do pregnant or recently pregnant women and health providers identify as enablers or barriers that support or limit successful management of GWG? What are women’s and health providers’ perceptions of GWG in relation to their health and the health of the child? What approaches have women and health providers tried to manage excess GWG? Outcomes include an identified preferred diet and exercise intervention for the planned clinical trial and information, which enables refinement of a locally acceptable implementation plan for the intervention. Collectively information from women and service providers enabled a comprehensive understanding of barriers, enablers and opportunities for the successful implementation of an intervention for GWG management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.015 |
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".