Evidence map›Paper›PMID 41883645›Full record

ArticleJournal of education and health promotion2026

Implementing and evaluating an integrated self-directed learning method for 1

Anagha Sahasrabuddhe, Shubhada Gade, Prathamesh H Kamble, Trupti Badwaik, Manju Chandankhede

Abstract read
In one paragraph

Article in Journal of education and health promotion, 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
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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

5 authors.

Anagha SahasrabuddheDepartment of Physiology, AIIMS Nagpur, Maharashtra, India.
Shubhada GadeDepartment of Physiology, Datta Meghe Medical College, Nagpur, Maharashtra, India.
Prathamesh H KambleDepartment of Physiology, AIIMS Nagpur, Maharashtra, India.
Trupti BadwaikDepartment of Anatomy, Datta Meghe Medical College, Nagpur, Maharashtra, India.
Manju ChandankhedeDepartment of Biochemistry, Datta Meghe Medical College, Nagpur, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs medical knowledge evolves rapidly, fostering lifelong learning skills among medical students is critical. The National Medical Commission's Competency-Based Medical Education curriculum emphasizes self-directed learning (SDL), allocating 200 hours across medical education, with 85 hours in the first phase. However, transitioning first-year students from traditional learning to SDL presents a challenge. This study explored an integrated SDL approach that combines anatomy, physiology, and biochemistry to enhance student readiness and interdisciplinary understanding. To evaluate the effectiveness of an integrated SDL approach on first-year medical students' understanding of jaundice and readiness for SDL using interdisciplinary collaboration and pre- and post-assessments. MATERIALS AND

methodsA quasi-experimental, pre-post design was conducted at Datta Meghe Medical College, Nagpur, involving collaboration among the Anatomy, Physiology, and Biochemistry departments. Participants (

resultsPost-test scores (mean: 12 ± 1.5) significantly improved from pre-test scores (mean: 8 ± 2;

conclusionsIntegrated SDL effectively enhanced knowledge, SDL readiness, and interdisciplinary understanding, aligning with the CBME goals. Future research should explore the long-term impact of diverse medical topics and settings.

Indexed as

Clinical reasoningcompetency-based educationcurriculum developmenteducational measurementinterdisciplinary teachinglearning readinessmedical educationproblem-based learningself-directed learning

Identifiers

PMID41883645
PMCPMC13012414

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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.