Factors associated with utilization of maternal serum screening for Down syndrome in mainland China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Knowledge of the factors that influence maternal serum screening (MSS) service utilization can be used to develop health policies to promote equitable access to MSS and further diagnostic tests. The purpose of this study was to find the factors associated with utilization of MSS as well as the current status of service utilization in mainland China. METHODS: This was a hospital-based cross-sectional study with respondents interviewed with a questionnaire designed based on Andersen's behavioral model. Descriptive statistics, univariate analysis, and multilevel logistic regression analysis were used to identify the factors associated with MSS utilization, and to explore potential methods to improve screening uptake. RESULTS: A total of 8110 women who had given birth within the previous 7 days in one of 111 participating institutions from six provinces in mainland China were interviewed. Approximately 36% of the participants had used MSS. Women between 20 and 35 years, who resided in urban areas, were educated, were in a stable occupation, who had health knowledge, who attended maternal preparation classes, who had received eight or more prenatal checkups, who were from a region of higher social economic status, and who delivered in a tertiary healthcare institution were significantly more likely to use MSS than their counterparts. As compared with other factors, insufficient education is the single most important demographic factor for service underutilization. CONCLUSIONS: Efforts should not only be made to target the population that underuses MSS, but the overall organization of MSS service delivery should be assessed during policy development to make access to MSS equitable to the entire population of mainland China.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".