A mixed-methods investigation of women’s experiences seeking pregnancy-related online nutrition information
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to describe women's processes for finding pregnancy-related nutrition information, their experiences seeking this information online and their ideas for improving internet sources of this information. METHODS: In total, 97 pregnant women completed an online quantitative questionnaire and 10 primiparous pregnant women completed semi-structured telephone interviews. Questionnaires and interviews asked participants to describe sources of pregnancy-related nutrition information; time of seeking; processes of searching online; experiences searching online; ideas for improving information found online. Survey data were analyzed using descriptive statistics and Chi square tests; interview data were analyzed using thematic analysis. RESULTS: Nearly all (96%) survey participants sought nutrition information online. Information was most commonly sought during the first trimester of pregnancy. Motivators for using the internet included convenience and lack of support from health care providers. Barriers to using online information included lack of trust, difficulty finding information and worry. Women adapted the information they found online to meet their needs and reported making positive changes to their diets. CONCLUSIONS: The internet is a key source of prenatal nutrition information that women report using to make positive dietary changes. Women would benefit from improved access to trustworthy internet sources, increased availability of information on different diets and health conditions, and increased support from health care providers.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".