Evidence map›Paper›PMID 39260821›Full record

SynthesisJournal of educational evaluation for health professions2024

Insights into undergraduate medical student selection tools: a systematic review and meta-analysis

Pin-Hsiang Huang, Arash Arianpoor, Silas Taylor, Jenzel Gonzales, Boaz Shulruf

RetractedAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of educational evaluation for health professions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Pin-Hsiang HuangOffice of Medical Education, Faculty of Medicine & Health, The University of New South Wales, Sydney, Australia.
Arash ArianpoorOffice of Medical Education, Faculty of Medicine & Health, The University of New South Wales, Sydney, Australia.
Silas TaylorOffice of Medical Education, Faculty of Medicine & Health, The University of New South Wales, Sydney, Australia.
Jenzel GonzalesOffice of Medical Education, Faculty of Medicine & Health, The University of New South Wales, Sydney, Australia.
Boaz ShulrufOffice of Medical Education, Faculty of Medicine & Health, The University of New South Wales, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEvaluating medical school selection tools is vital for evidence-based student selection. With previous reviews revealing knowledge gaps, this meta-analysis offers insights into the effectiveness of these selection tools.

methodsA systematic review and meta-analysis were conducted applying the following criteria: peer-reviewed articles available in English, published from 2010 and which include empirical data linking performance in selection tools with assessment and dropout outcomes of undergraduate entry medical programs. Systematic reviews, meta-analyses, general opinion pieces, or commentaries were excluded. Effect sizes (ESs) of the predictability of academic and clinical performance within and by the end of the medicine program were extracted, and the pooled ESs were presented.

resultsSixty-seven out of 2,212 articles were included, which yielded 236 ESs. Previous academic achievement predicted medical program academic performance (Cohen’s d=0.697 in early program; 0.619 in end of program) and clinical exams (0.545 in end of program). Overall aptitude tests predicted academic achievement in both the early and last years (0.550 & 0.371, respectively). Within aptitude tests, verbal reasoning and quantitative reasoning best predicted academic achievement in the early program (0.704 & 0.643, respectively). Neither panel interviews, multiple mini-interviews, nor situational judgement tests (SJT) yielded statistically significant pooled ES.

conclusionCurrent evidence suggests that learning outcomes are predicted by previous academic achievement and aptitude tests. The predictive value of SJT and topics such as selection algorithms, features of interview (e.g., content of the questions) and the way the interviewers’ reports are used, warrant further research.

Indexed as

Education, Medical, UndergraduateSchool Admission CriteriaStudents, MedicalAcademic PerformanceAcademic SuccessAptitude TestsHumansSchools, MedicalAcademic performanceAlgorithmsAptitude testsMedical schoolsSchool admission criteria

Identifiers

PMID39260821
PMCPMC11494217

What OpenQuestion holds

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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.