Is bigger better? Assessment of self-reported and researcher-collected data on maternal health care quality among high-case-load facilities in Uttar Pradesh: a mixed-methods study
Bibliographic record
Abstract
BackgroundIndia's most populous state, Uttar Pradesh, has the country's second highest maternal mortality ratios, at 285 compared with the national maternal mortality ratio of 167. Reports of disrespect, abuse, and other types of mistreatment are also commonly reported by both the scientific community and popular media. Across nearly 750 facilities in Uttar Pradesh, the SPARQ Quality-Plus (Q+) study aims to understand which high-case-load facilities provide better maternal health care and why. Our objective was to identify whether “better” quality at facilities varied by the measures used to assess maternal health clinical quality and person-centred care quality in Uttar Pradesh. We compared self-reported government data with delivery patient survey and health provider interview data on maternal health infrastructure, service delivery, and person-centred care outcomes.MethodsThe study sites were sampled based on self-reported performance data, stratified by facility type and geography. Study materials included a health service readiness checklist completed by 727 health facilities during early 2017 and caesarean section and delivery outcome data by quarter from these facilities for 2015 and 2016. Both sources are reported by the Uttar Pradesh National Health Mission (NHM). These secondary data sources were analysed to create a composite quality score used to select 20 high-performing and 20 low-performing sites from among 246 high-volume facilities (>200 deliveries/month). At these 40 sites, quality was assessed using quantitative and qualitative primary data collection with delivery patients (n=2018) and providers (n=251) and health service readiness checklists (n=40).FindingsAcross all the study facilities (n=40), little correlation existed between the self-reported and researcher-collected measures of clinical quality. Yet self-reported measures do not necessarily report better levels of quality. For example, our researcher-collected data showed that facility-reported emergency obstetric care was more common than self-reported emergency obstetric care (n=29 [73%] vs n=21 [54%] in the government reported data). We found a strong negative correlation (t=–2·05; p<0·05) between clinical and person-centred care quality—facilities with higher clinical quality tend to have worse person-centred care. We found a seven-fold increase in verbal abuse as clinical quality improves (t=–7·71; p<0·001). Women were less likely to deliver with an unskilled birth attendant in higher-quality facilities (t=–3·61; p<0·001). However, even in high-performing facilities (n=20), 132 (13%) of 1008 women report delivering alone, with a friend, relative, or hospital cleaner.InterpretationAlthough district hospitals and other higher-level referral facilities provide better clinical care than smaller centres and hospitals in Uttar Pradesh, they provide worse patient-centred care and are more likely to be sites of abuse and disrespect. Delayed health-seeking during pregnancy and resistance to referral to higher-level facilities is a serious issue in Uttar Pradesh. Improving patient care in larger maternity centres is therefore both important and has the potential to address an underlying driver of morbidity and mortality. On the basis of these findings, we intend to work with the Uttar Pradesh NHM to enhance person-centred care to mothers and their newborn babies in high-volume facilities and to ultimately improve overall maternal and neonatal health outcomes in Uttar Pradesh and across India.FundingBill & Melinda Gates Foundation.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".