Evidence map›Paper›PMID 41255919›Full record

ArticleCanadian medical education journal2025

Assessing the impact of virtual learning on family medicine trainees' medical knowledge using progress tests: a retrospective cohort study.

Karina Prucnal, Stuart Murdoch, Kulamakan Kulasegaram, Fok-Han Leung

Abstract read
In one paragraph

Article in Canadian medical education journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Authorship in medical education.Canadian medical education journal · 2025
    Article
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

4 authors.

Karina PrucnalDepartment of Family & Community Medicine, University of Toronto, Ontario, Canada.
Stuart MurdochDepartment of Family & Community Medicine, University of Toronto, Ontario, Canada.
Kulamakan KulasegaramDepartment of Family & Community Medicine, University of Toronto, Ontario, Canada.
Fok-Han LeungDepartment of Family & Community Medicine, University of Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Progress testing provides residents with an opportunity to identify strengths and weaknesses, encouraging self-directed learning. The University of Toronto's Department of Family & Community Medicine administers the Family Medicine Mandatory Assessment of Progress (FM-MAP) biannually to track resident competency and medical knowledge. Our aim was to determine the impact of virtual learning on Family Medicine residents. Methods: We administered previous iterations of the FM-MAP to the virtual learning cohort and compared scores to those of the in-person cohort between October 2020 - Spring 2022. Results: There were no statistically significant differences between in-person and virtual cohorts of first- and second-year postgraduate family medicine trainees regarding their overall FM-MAP score. Second-year family medicine trainees outperformed first year trainees in both cohorts. Conclusion: The study found no significant effect on the scores of first- and second-year family medicine trainees caused by the shift to virtual learning, suggesting medical curricula can incorporate virtual learning without compromising trainee progress, offering flexibility in medical education. Future studies could explore its applicability across different residency programs and long-term effects on clinical performance.

Indexed as

Clinical CompetenceEducational MeasurementFamily PracticeInternship and ResidencyAdultCurriculumFemaleHumansMaleRetrospective Studies

Identifiers

PMID41255919
PMCPMC12620748

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.