Students with Disabilities in Clinical Internships: Perspectives of College Faculty, Accessibility Advisors, and Students
Bibliographic record
Abstract
We present findings from an interview study concerning Canadian college students with disabilities in clinical internships. The study explored the perspectives of seven college faculty internship supervisors, eight accessibility advisors, and 14 students with diverse disabilities who were currently doing - or had recently completed - a required internship for their program. Thematic analysis was used to uncover key themes related to facilitators and barriers relevant to accessibility, knowledge, communication, disclosure / self-identification, policies / legislation, support strategies, and useful technologies. Highlights of our results indicate that accommodations that are typically available in college classrooms are not appropriate for most internship settings, that policies and procedure manuals related to accessibility supports / accommodations in internships are scarce, and that communication among faculty, students and accessibility advisors is poor and do not typically include on-site clinical supervisors. In addition, we found that students are often reluctant to self-identify as having a disability because of fear of stigma and professional concerns. A variety of technologies that can be useful in internship settings was identified, but participants noted that internship settings were often reluctant to use these because of concerns with patient confidentiality and biohazard safety. We recommend that colleges develop Equity, Diversity, Inclusion and Accessibility (EDIA) policies that reflect internships and that communication structures be developed that allow all stakeholders to participate.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".