Assessing quality of maternity care in Hungary: expert validation and testing of the mother-centered prenatal care (MCPC) survey instrument
Bibliographic record
Abstract
BACKGROUND: Instruments to assess quality of maternity care in Central and Eastern European (CEE) region are scarce, despite reports of poor doctor-patient communication, non-evidence-based care, and informal cash payments. We validated and tested an online questionnaire to study maternity care experiences among Hungarian women. METHODS: Following literature review, we collated validated items and scales from two previous English-language surveys and adapted them to the Hungarian context. An expert panel assessed items for clarity and relevance on a 4-point ordinal scale. We calculated item-level Content Validation Index (CVI) scores. We designed 9 new items concerning informal cash payments, as well as 7 new "model of care" categories based on mode of payment. The final questionnaire (N = 111 items) was tested in two samples of Hungarian women, representative (N = 600) and convenience (N = 657). We conducted bivariate analysis and thematic analysis of open-ended responses. RESULTS: Experts rated pre-existing English-language items as clear and relevant to Hungarian women's maternity care experiences with an average CVI for included questions of 0.97. Significant differences emerged across the model of care categories in terms of informal payments, informed consent practices, and women's perceptions of autonomy. Thematic analysis (N = 1015) of women's responses identified 13 priority areas of the maternity care experience, 9 of which were addressed by the questionnaire. CONCLUSIONS: We developed and validated a comprehensive questionnaire that can be used to evaluate respectful maternity care, evidence-based practice, and informal cash payments in CEE region and beyond.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".