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Record W2794037386 · doi:10.1016/s2214-109x(18)30175-x

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

2018· article· en· W2794037386 on OpenAlexaboutno aff
Beth Phillips, Fnu Kajal, Dominic Montagu, Aarti Kumar, Vishwajeet Kumar

Bibliographic record

VenueThe Lancet Global Health · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsHealth facilityChecklistMedicineEnvironmental healthHealth careReproductive healthGovernment (linguistics)Quarter (Canadian coin)Family medicineNursingPsychologyGeographyHealth servicesPopulation

Abstract

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Background India'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. Methods The 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). Findings Across 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. Interpretation Although 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. Funding Bill & Melinda Gates Foundation.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.128
GPT teacher head0.521
Teacher spread0.393 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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