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Record W4416851711 · doi:10.1186/s12909-025-08197-1

Differential attainment within medical education: a systematic review

2025· review· en· W4416851711 on OpenAlexaboutno aff
Cynthia Cunningham, Lorraine Thong, David Mockler, Martina Hayes, Clíona Ní Cheallaigh, Susan M. Smith

Bibliographic record

VenueBMC Medical Education · 2025
Typereview
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupSocioeconomic statusSystematic reviewNarrative reviewMEDLINEScale (ratio)Educational attainmentDifferential (mechanical device)

Abstract

fetched live from OpenAlex

BACKGROUND: Differential attainment describes the systemic differences in outcomes when grouping cohorts by protected characterises and socioeconomic backgrounds. To date, most research studies have focused on a small number of characteristics and outcomes. This systematic review aimed to explore the association between a range of student/trainee characteristics and differential attainment in undergraduate and postgraduate medical education. METHODS: A systematic review was conducted and reported using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines with searches conducted in May 2023. Outcome measures were assessment, progression through training, and qualification in medical students or trainee doctors. Specific characteristics included were age, disability, entry route to medicine, ethnicity, gender, religion and socio-economic status. The Cochrane RoB tool, the Newcastle-Ottawa Scale and the GRADE approach were applied. A narrative synthesis was conducted due to heterogeneity between studies. RESULTS: Ninety-five studies were included. The majority were observational. There were over 381,815 medical student and 395,191 trainee doctor participants across all studies. The certainty across included studies was low and results were heterogeneous. Most results focused on assessment, followed by progression, then qualification. Gender, ethnicity and age were most commonly studied. Mixed results were noted for gender with results suggesting an advantage for undergraduate females however, females took longer to complete postgraduate training, and males were more likely to be on a specialist register. Minoritised ethnicity was consistently associated with lower attainment across a large number of studies. Younger doctors consistently did better in postgraduate assessments. Only 32 studies examined other characteristics with mixed results for disability and entry routes to medicine. The impact of socioeconomic status varied, but lower status was associated with lower attainment at later career stages. CONCLUSION: Differential attainment is a complex, context-dependent phenomenon with multiple contributory factors that are challenging to disentangle. Despite low certainty of evidence, this review suggests that many characteristics do impact on outcomes in medical education. This warrants further investigation and indicates a need for monitoring and targeted differential attainment interventions to ensure equitable outcomes for medical students and doctors in training. REGISTRATION: This systematic review is registered on PROSPERO, accessible at https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023426933 . PROSPERO has been updated to reflect the progression of this review.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.060
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.010
Bibliometrics0.0110.013
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.456
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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