Evidence map›Paper›PMID 42449315›Full record

ArticleBMC medical education2026

Impact of an integrated interdisciplinary team-based learning session of medicine and health sector programs at Galala University.

Ahmed Nour Eldin Hassan, Noha N Lasheen

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Ahmed Nour Eldin HassanFaculty of Medicine, 6th October University, Giza, Egypt.
Noha N LasheenFaculty of Medicine, Galala University, Suez, Egypt. nohalasheen@gu.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimActive learning, especially through interdisciplinary approaches, is increasingly recognized as a means to enhance the effectiveness of health promotion programs. This study evaluated the impact of introducing a team-based learning (TBL) session, structured as an interdisciplinary experience, on students from health-related faculties. The primary aim was to demonstrate the effects of an interdisciplinary TBL session across five faculties at Galala University.

methodsUsing a quasi-experimental pre-post design, an interdisciplinary TBL session was conducted at Galala University, involving students from the Faculties of Medicine, Pharmacy, Physical Therapy, Applied Health Sciences, and Nursing. Twenty-five students volunteered and were divided into five teams. The session, centered on bronchial asthma, followed a three-phase structure: an initial mini-lecture, readiness assurance tests (administered individually and in teams), a debriefing session, and a group application task. At the third phase, students completed a satisfaction survey.

resultsTeam-based readiness assurance test (t-RAT) scores consistently exceeded individual readiness assurance test (i-RAT) scores across nine multidisciplinary questions, indicating improved performance through teamwork. The satisfaction survey revealed that the majority of students perceived benefits from participating in the interdisciplinary session.

conclusionTBL may play a valuable role in adopting hands-on involvement, peer-assisted learning, critical thinking, and could enrich the student-centered learning through collaborative problem-solving in bronchial asthma.

Indexed as

Patient Care TeamProblem-Based LearningFemaleHumansMaleProgram EvaluationInterdisciplinary sessionMedicine and Health sectorStudent-centered LearningTeam-based learning.

Identifiers

PMID42449315
PMCPMC13366941

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Registered trials

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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.