Evidence map›Paper›PMID 42316388›Full record

ArticleAdvances in simulation (London, England)2026

Impact of interprofessional blended learning with low-fidelity simulation on neonatal resuscitation skills among interns: a quasi-experimental study in Tanzania.

Ambroce Stephen, Angelina A Joho, Vicent Bankanie

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Article in Advances in simulation (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ambroce StephenDepartment of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania. ambroceste2012@gmail.com.
Angelina A JohoDepartment of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania.
Vicent BankanieDepartment of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the national rollout of the Helping Babies Breathe (HBB) program, neonatal mortality due to birth asphyxia remains a major challenge in Tanzania. Evidence shows that there is inadequate competence (including knowledge, skills, and attitudes) in newborn resuscitation among medical and nursing interns as they transition into practice. The traditional method of a one-day instructor-led HBB model provides little hands-on practice. Integrating low-fidelity simulation with blended learning may contribute to better transfer of learning and offer a more sustainable model for skills retention.

objectiveTo evaluate the efficacy of interprofessional low-fidelity simulation with a blended-learning approach among medical and nursing interns' HBB knowledge and skills in Dar es Salaam.

methodsA controlled quasi-experimental design was used with 128 participants. Stratified random sampling by profession was used to select participants for the two groups (64 in the intervention group, 64 in the control group). The intervention group received a 2-h instructor-led online session and two days (16 h total) of hands-on low-fidelity simulation, guided by the HBB 2nd Edition curriculum (which emphasises the Golden Minute, preparation for birth, initial steps including stimulation and warming, bag-mask ventilation, and post-resuscitation care through interactive case-based scenarios and paired practice). The control group received reading materials and continued with their business-as-usual practice. Knowledge (17-item MCQ) and skills (18-item checklist) were assessed at baseline, immediately after intervention, and six weeks post-intervention for both groups. Data were analysed using IBM SPSS version 27, with an independent t-test and a linear mixed model (LMM). β coefficients represent adjusted mean differences from linear mixed models.

resultsThe groups were similar at baseline. The intervention group showed significant improvements compared with the control group. Knowledge score increased by β = 5.39 immediately post-intervention post-training, and by β = 3.90 in six weeks (both p < 0.001). Skills improved by β = 5.36 immediately and β = 3.47 at six weeks (both p < 0.001).

conclusionLow-fidelity simulation with blended learning significantly improved HBB knowledge and skills among interns, with effects sustained at six weeks. This approach is practical and scalable for strengthening newborn resuscitation capacity in resource-limited settings.

trial registrationTrial registered to Pan African Trial Registry with Reg no PACTR202512785351089.

Indexed as

Blended learningDar es SalaamHelping Babies BreatheLow-fidelity simulationMedical and nursing internsNeonatal resuscitationTanzania

Identifiers

PMID42316388
PMCPMC13548496

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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.