Evidence map›Paper›PMID 38575944›Full record

ArticleBMC pregnancy and childbirth2024

Appraising LaQshya's potential in measuring quality of care for mothers and newborns: a comprehensive review of India's Labor Room Quality Improvement Initiative.

Shalini Singh, Zabir Hasan, Deepika Sharma, Amarpreet Kaur, Deeksha Khurana, J N Shrivastava, Shivam Gupta

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
13.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 4 countries.

Shalini SinghDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA. ssing120@jhu.edu.ORCID http://orcid.org/0009-0008-0144-2587
Zabir HasanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Deepika SharmaNational Health Systems Resource Center, New Delhi, India.
Amarpreet KaurDepartment of Epidemiology, Biostatistics and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Deeksha KhuranaJohns Hopkins India Pvt Limited, New Delhi, India.
J N ShrivastavaNational Health Systems Resource Center, New Delhi, India.
Shivam GuptaThe Global Fund, Geneva, Switzerland.
Johns Hopkins University · USNational Health Systems Resource Centre · INGlobal Fund to Fight AIDS, Tuberculosis and Malaria · CHUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPoor intrapartum care in India contributes to high maternal and newborn mortality. India's Labor Room Quality Improvement Initiative (LaQshya) launched in 2017, aims to improve intrapartum care by minimizing complications, enforcing protocols, and promoting respectful maternity care (RMC). However, limited studies pose a challenge to fully examine its potential to assess quality of maternal and newborn care. This study aims to bridge this knowledge gap and reviews LaQshya's ability to assess maternal and newborn care quality. Findings will guide modifications for enhancing LaQshya's effectiveness.

methodsWe reviewed LaQshya's ability to assess the quality of care through a two-step approach: a comprehensive descriptive analysis using document reviews to highlight program attributes, enablers, and challenges affecting LaQshya's quality assessment capability, and a comparison of its measurement parameters with the 352 quality measures outlined in the WHO Standards for Maternal and Newborn Care. Comparing LaQshya with WHO standards offers insights into how its measurement criteria align with global standards for assessing maternity and newborn care quality.

resultsLaQshya utilizes several proven catalysts to enhance and measure quality- institutional structures, empirical measures, external validation, certification, and performance incentives for high-quality care. The program also embodies contemporary methods like quality circles, rapid improvement cycles, ongoing facility training, and plan-do-check, and act (PDCA) strategies for sustained quality enhancement. Key drivers of LaQshya's assessment are- leadership, staff mentoring, digital infrastructure and stakeholder engagement from certified facilities. However, governance issues, understaffing, unclear directives, competency gaps, staff reluctance towards new quality improvement approaches inhibit the program, and its capacity to enhance quality of care. LaQshya addresses 76% of WHO's 352 quality measures for maternal and newborn care but lacks comprehensive assessment of crucial elements: harmful labor practices, mistreatment of mothers or newborns, childbirth support, and effective clinical leadership and supervision.

conclusionLaQshya is a powerful model for evaluating quality of care, surpassing other global assessment tools. To achieve its maximum potential, we suggest strengthening district governance structures and offering tailored training programs for RMC and other new quality processes. Furthermore, expanding its quality measurement metrics to effectively assess provider accountability, patient outcomes, rights, staff supervision, and health facility leadership will increase its ability to assess quality improvements.

Indexed as

Maternal Health ServicesQuality ImprovementFemaleHumansInfant, NewbornMothersParturitionPregnancyQuality of Health CareIntrapartum careMaternal and newborn careMaternal and newborn care assessmentQuality improvementQuality of care assessment

Identifiers

PMID38575944
PMCPMC10993574
OpenAlexW4393935412

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.