Willingness of pregnant women to participate in a birth cohort study in China
Bibliographic record
Abstract
OBJECTIVE: To determine the willingness of pregnant women in Guangzhou, China, to participate in a large-scale birth cohort study. METHODS: A cross-sectional survey was conducted of 526 pregnant women who attended their first prenatal class at Guangzhou Women and Children's Medical Center, Guangzhou, China, between September 21 and November 15, 2011. Information on demographic characteristics, willingness to participate, and preferences regarding collection procedures and incentives were analyzed. RESULTS: In all, 47.9% of the women were willing to participate in a birth cohort study, whereas 23.0% refused and 29.1% were unsure. The majority of the women willing to participate (95.2%-98.4%) accepted the use of non-invasive data collection methods except for stool collection, and 85.9% would allow their offspring to participate in long-term follow-up. Willingness to participate rose to 85.2% when non-monetary incentives were offered. The most popular incentive was assessment of child development. CONCLUSION: The willingness of pregnant Chinese women to participate in long-term observational research was similar to that reported in high-income countries. Non-monetary incentives improved their level of willingness, a finding that might inform future maternal and child health research in low- and middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".