Evidence map›Paper›PMID 39807134›Full record

ArticleCanadian medical education journal2024

Evaluating the

Imaan Zera Kherani, Clara Osei-Yeboah, Maham Bushra, Meera Mahendiran, Happy Inibhunu, Maria Mylopoulos, Marcus Law

Abstract read
In one paragraph

Article in Canadian medical education journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. The CMEJ in phases: closing out 2024, closing in on 2030.Canadian medical education journal · 2024
    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

7 authors.

Imaan Zera KheraniTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Clara Osei-YeboahTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Maham BushraTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Meera MahendiranTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Happy InibhunuDivision of Neurosurgery, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, University of Western Ontario, Ontario, Canada.
Maria MylopoulosTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Marcus LawTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Equity-deserving groups are communities marginalized from institutional power by oppressive forces (e.g., racism, sexism, homophobia, ableism). Methods: Between February and March 2022, we recruited medical students across all levels of training from English-speaking Canadian medical schools using email listservs and social media. We disseminated a cross-sectional questionnaire assessing demographics, knowledge gained from podcast engagement, attitudes towards podcasts as a tool for mentorship, and psychological/emotional gains from the podcast content. We conducted descriptive and frequency analyses of quantitative data and applied thematic analysis to qualitative data. Results: Thirty-eight individuals completed the entire survey from all levels of training, with 97% self-identifying with at least one equity-deserving group. 100% agreed that the Conclusions: Podcasts can serve as a medium for accessible equity-centred mentorship. By disseminating multiple underrepresented narratives in medicine, the

Indexed as

MentorsStudents, MedicalWebcasts as TopicAdultCanadaCross-Sectional StudiesCultural DiversityFemaleHumansMaleSchools, MedicalSurveys and Questionnaires

Identifiers

PMID39807134
PMCPMC11725005

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.