Strategies to increase accessibility for students with disabilities in health professional programs: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify strategies in the literature that may increase the accessibility of health professional programs for students with disabilities. INTRODUCTION: The number of students with disabilities in health professional programs is increasing. However, since students with disabilities experience barriers to education, their retention rates are potentially lower and their attrition rates are potentially higher compared with their non-disabled peers. INCLUSION CRITERIA: Academic and gray literature that identifies strategies intended to increase accessibility to health professional programs for students with disabilities will be included. Only articles published from 2000 onward will be considered for inclusion. METHODS: This review will follow the JBI methodology for scoping reviews. Databases to be searched will include Embase (Ovid), MEDLINE (Ovid), PsycINFO (Ovid), CINAHL (EBSCOhost), ERIC (ProQuest), and Web of Science. Gray literature will be searched for using Google. Websites of known disability organizations will also be searched. There will be no language limitations. Paired reviewers will independently screen titles and abstracts, and then full-text articles. Data will be extracted using a tool developed by the reviewers. The extracted data will be synthesized and reported in tabular format, accompanied by a narrative summary connecting the results to the objective of the review. REVIEW REGISTRATION: Open Science Framework https://osf.io/bsyrt.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.098 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".