Accommodations in the Assessment of Health Professionals at Entry-to-Practice: A Scoping Review
Bibliographic record
Abstract
This scoping review examines the available evidence supporting accommodation use in the assessment of health professionals with disabilities in licensing contexts. While test accommodations are a protected right under antidiscrimination legislation, the peer-reviewed evidence informing their use is contested and widely dispersed. Furthermore, the ramifications of accommodation misuse are significant, including human rights violations and increased risks to patients. As such, this study addressed two research questions: 1) What is the current state of literature on accommodation use in the assessment of health professionals? and 2) What programs of research would address stakeholders’ concerns about the use of accommodations in the assessment of those professionals? Systematic searches of five prominent databases identified 15 articles for analysis. Several major themes emerged from that analysis: interpreting legislation, administration and process, relationships between education and licensure, and psychometrics and test development. Stakeholder consultation revealed that stakeholders face challenges managing accommodation requests and defining reasonable accommodations. While there is a paucity of literature on the topic overall, especially of an empirical nature, this study mapped the available evidence and laid the foundation for future studies by delineating the gaps in the scholarly literature as defined by stakeholders’ needs.
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 | 0.024 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".