Accommodating Students with Disabilities: Fieldwork Educators’ Experiences
Bibliographic record
Abstract
Background. Fieldwork is an essential part of experiential learning in occupational therapy education. Fieldwork educators identify limits on reasonable accommodation and difficulty implementing disability-related accommodations. Student occupational therapists with disabilities report discrimination from within the profession, including inflexible fieldwork environments. Purpose. To understand the experiences of occupational therapy fieldwork educators in Canada in accommodating students with disabilities and to develop action-oriented practice recommendations. Method. In this interpretive description study, we interviewed 11 fieldwork educators about their experiences accommodating students with disabilities. Interviews were recorded, transcribed, and analyzed using a constant comparative approach. Findings. Educators emphasized a meta-theme of “Learning” when asked about disability-related accommodations. Three subthemes about student learning emerged: 1. Educators focused on “Student Learning in Preparation for Professional Practice” rather than their fieldwork setting only; 2. Educators were “Using Occupational Therapy Skills for Student Learning” in fieldwork; and 3. Educators recognized that their professional and personal “Context Influences Student Learning.” Conclusion. Fieldwork educators can work with students to align their accommodations with required learning outcomes for professional practice and use their occupational therapy skills to assist with implementation. Fieldwork educators require time and other supports to work effectively with all students.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".