An assessment of patient information channels and knowledge of physical activity and nutrition during pregnancy
Bibliographic record
Abstract
BACKGROUND: Excessive weight gain during pregnancy increases the risk for obesity in mother and child. Healthy eating and physical activity may help prevent excessive gestational weight gain and minimize offspring risk of developing obesity, diabetes and cardiovascular disease. Our goal was to determine the information channels used by pregnant women to obtain information on nutrition and exercise. METHODS: We collected information about their knowledge of physical activity and nutrition during pregnancy and assessed their satisfaction with this information to identify factors that may be improved upon when designing a behavioural intervention. An anonymous, voluntary questionnaire was completed by 147 pregnant women to identify the proportion who are currently receiving information about exercise from their care provider. RESULTS: The primarily Caucasian sample (age: 30.9 ± 4.2, weeks gestation: 21.4 ± 9.4) completed the survey. A total of 86% are willing to participate in a lifestyle intervention trial. Personal health and the health of their child were cited as top reasons for participation. Most women were not informed as to the importance of appropriate pregnancy-specific energy intake or made aware of their own personal healthy gestational weight gain targets. A total of 63% report receiving some form of information on physical activity during pregnancy. Of those who do not, almost all (93%) would like to receive this information from a care provider. Overall, 88% of women consider it safe to exercise when pregnant. DISCUSSION: Given their responses, nutrition and exercise information offered through a lifestyle intervention during pregnancy may increase healthy behaviours and warrants clinical investigation.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".