Strategies for Increasing Accessibility and Equity in Health and Human Service Educational Programs: Protocol for a National, Mixed Methods Study
Bibliographic record
Abstract
Introduction: Despite legislation mandating accommodation policies in higher education, support for learners with disabilities is often not implemented within health and human services (HHS) education programs, particularly in fieldwork settings. This paper will describe the protocol of a study aimed to (a) explore challenges and opportunities of current practices for supporting learners living with disabilities in a fieldwork context, across 10 HHS programs; and (b) develop, pilot and evaluate innovative accessibility practices to decrease existing barriers faced by educators and learners. Method: Using a critical disability studies framework, we designed a national, multi-profession, mixed methods design. Data are collected through interviews (qualitative) and an online survey (quantitative) that participants complete prior to the interview. Additionally, an online mapping diary is used to facilitate the understanding of accessibility in fieldwork education from the perspective of the learners. Participants include learners living with disabilities, academic fieldwork coordinators, fieldwork educators, accessibility advisors and professional organizations representatives. Implications: Learners living with disabilities navigate systemic barriers: (a) the additional “work of being a disabled learner”, during a rigorous academic program, and (b) absent or inadequate fieldwork accommodations. Exploring those systemic barriers as faced by all partners offers the potential to develop strategies and tools to foster inclusive and accessible HHS education.
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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 | 0.095 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".