Exploring the accuracy of self-reported maternal and newborn care in select studies from low and middle-income country settings: do respondent and facility characteristics affect measurement?
Bibliographic record
Abstract
Abstract Background Accurate data on the receipt of essential maternal and newborn health interventions is necessary to interpret and address gaps in effective coverage. Validation results of commonly used content and quality of care indicators routinely implemented in international survey programs vary across settings. We assessed how respondent and facility characteristics influenced the accuracy of women’s recall of interventions received in the antenatal and postnatal periods. Methods We synthesized reporting accuracy using data from a known sample of validation studies conducted in Sub-Saharan Africa and Southeast Asia, which assessed the validity of women’s self-report of received antenatal care (ANC) (N = 3 studies, 3,169 participants) and postnatal care (PNC) (N = 5 studies, 2,462 participants) compared to direct observation. For each study, indicator sensitivity and specificity are presented with 95% confidence intervals. Univariate fixed effects and bivariate random effects models were used to examine whether respondent characteristics (e.g., age group, parity, education level), facility quality, or intervention coverage level influenced the accuracy of women’s recall of whether interventions were received. Results Intervention coverage was associated with reporting accuracy across studies for the majority (9 of 12) of PNC indicators. Increasing intervention coverage was associated with poorer specificity for 8 indicators and improved sensitivity for 6 indicators. Reporting accuracy for ANC or PNC indicators did not consistently differ by any other respondent or facility characteristic. Conclusions High intervention coverage may contribute to higher false positive reporting (poorer specificity) among women who receive facility-based maternal and newborn care while low intervention coverage may contribute to false negative reporting (lower sensitivity). While replication in other country and facility settings is warranted, results suggest that monitoring efforts should consider the context of care when interpreting national estimates of intervention coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".