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Record W4410231057 · doi:10.1080/0142159x.2025.2499093

Strategies to increase accessibility for students with disabilities in health professional education programs: A scoping review: BEME Review No. 94

2025· article· en· W4410231057 on OpenAlexaff
Shaminder Dhillon, M. Roque, Paige Maylott, Dina Brooks, Sarah Wojkowski

Bibliographic record

VenueMedical Teacher · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedical educationPsychologyMEDLINEMedicinePedagogyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Despite legislative changes, students with disabilities experience lower retention and higher attrition in health professional programs (HPP), compared to nondisabled peers. The purpose of this study was to identify strategies in the literature that may improve HPP accessibility for disabled students. METHODS: The Joanna Briggs Institute methodology was applied to this scoping review. Five databases, four Google domains, and five websites of organizations that promote student accessibility were searched. Reviewers applied inclusion and exclusion criteria for title and abstract screening; conducted full-text reviews; and extracted and analyzed data using counts, frequencies, coding, and categorizing to identify strategies. RESULTS: Strategies to improve HPP accessibility were reported most often in literature from the USA, and by nursing and medical professions. The most salient strategy was 'types of accommodations' provided by HPP, followed by 'education, critical reflection and culture change for educators and staff.' CONCLUSIONS: While types of accommodations are reported often, they may not be widely applicable nor generalizable given the number of students with disabilities are increasing and the need to consider each student's unique accommodations. A multi-pronged approach of education, critical reflection, and culture change for educators and staff may support shifting HPP more broadly towards embracing inclusivity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.535
Teacher spread0.463 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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