Evidence map›Paper›PMID 28754111›Full record

ArticleBMC pregnancy and childbirth2017

Care of the mother-infant dyad: a novel approach to conducting and evaluating neonatal resuscitation simulation training in Bihar, India.

Brennan Vail, Hilary Spindler, Melissa C Morgan, Susanna R Cohen, Amelia Christmas, Pramod Sah, Malay B Shah, Aritra Das, Dilys M Walker

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
5.3field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 19 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

9 authors at 3 institutions in 2 countries.

Brennan VailDepartment of Pediatrics, University of California San Francisco, 550 16th Street, 4th Floor, 4551, Box 0110, San Francisco, CA, 94143, USA. Brennan.Vail@ucsf.edu.ORCID http://orcid.org/0000-0002-8304-6658
Hilary SpindlerGlobal Health Sciences, University of California San Francisco, 550 16th St, San Francisco, CA, 94158, USA.
Melissa C MorganGlobal Health Sciences, University of California San Francisco, 550 16th St, San Francisco, CA, 94158, USA.
Susanna R CohenCollege of Nursing, University of Utah, 10 South 2000 East, Salt Lake City, UT, 84112, USA.
Amelia ChristmasPronto International, State RMNCH+A Unit, C-16 Krishi Nagar, A.G. Colony, Patna, Bihar, 80002, India.
Pramod SahCARE India, 14 Patliputra Colony, Patna, Bihar, 800013, India.
Malay B ShahCARE India, 14 Patliputra Colony, Patna, Bihar, 800013, India.
Aritra DasCARE India, 14 Patliputra Colony, Patna, Bihar, 800013, India.
Dilys M WalkerDepartment of Obstetrics and Gynecology and Reproductive Services, University of California San Francisco, 1001 Potrero Ave, San Francisco, CA, 94110, USA.
University of California, San Francisco · USCare India · INUniversity of Utah · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the global under-five mortality rate declines, an increasing percentage is attributable to early neonatal mortality. A quarter of early neonatal deaths are due to perinatal asphyxia. However, neonatal resuscitation (NR) simulation training in low-resource settings, where the majority of neonatal deaths occur, has achieved variable success. In Bihar, India, the poorest region in South Asia, there is tremendous need for a new approach to reducing neonatal morality.

methodsThis analysis aims to assess the impact of a novel in-situ simulation training program, developed by PRONTO International and implemented in collaboration with CARE India, on NR skills of nurses in Bihar. Skills were evaluated by clinical complexity of the simulated scenario, which ranged from level 1, requiring NR without a maternal complication, to level 3, requiring simultaneous management of neonatal and maternal complications. A total of 658 nurses at 80 facilities received training 1 week per month for 8 months. Simulations were video-recorded and coded for pre-defined clinical skills using Studiocode™.

resultsA total of 298 NR simulations were analyzed. As simulation complexity increased, the percentage of simulations in which nurses completed key steps of NR did not change, even with only 1-2 providers in the simulation. This suggests that with PRONTO training, nurses were able to maintain key skills despite higher clinical demands. As simulation complexity increased from level 1 to 3, time to completion of key NR steps decreased non-significantly. Median time to infant drying decreased by 7.5 s (p = 0.12), time to placing the infant on the warmer decreased by 21.7 s (p = 0.27), and time to the initiation of positive pressure ventilation decreased by 20.8 s (p = 0.12). Nevertheless, there remains a need for improvement in absolute time elapsed between delivery and completion of key NR tasks.

conclusionsPRONTO simulation training enabled nurses in Bihar to maintain core NR skills in simulation despite demands for higher-level triage and management. Although further evaluation of the PRONTO methodology is necessary to understand the full scope of its impact, this analysis highlights the importance of conducting and evaluating simulation training in low-resource settings based on simultaneous care of the mother-infant dyad.

Indexed as

AdultClinical CompetenceFemaleHumansIndiaInfantInfant MortalityInfant WelfareMidwiferyPerinatal CarePovertyPregnancyRural Health ServicesSimulation TrainingYoung AdultEarly neonatal mortalityMother-infant dyadNeonatal resuscitationSimulation training

Identifiers

PMID28754111
PMCPMC5534030
OpenAlexW2740774543

What OpenQuestion 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 OpenQuestion graph.