Bridging Policy-Curriculum-Accommodations: A Nursing Education Initiative
Bibliographic record
Abstract
Supporting student academic accommodations (AAs) is an increasingly prevalent challenge for post-secondary nursing instructors in Ontario, with the growing number of students in nursing programs with mental health or medical disabilities. Historically, nursing programs have denied access to potential students with disabilities; today, the rigorous nursing program structure and the lack of support for instructors remain realities, even with the Ontario Human Rights Code and administrators' policies. The nursing faculty team (NFT) at Top College (TC) (pseudonym) must uphold educational and clinical expectations while assuring student competencies and curriculum integrity. In this comprehensive dissertation-in-practice (DiP), I explore bridging gaps in policy, curriculum, and the growing complexity of AAs. This proposed initiative combines a universal design for learning (UDL) framework with a nursing solution (NS) (UDL-NS), which includes the creation of a nursing accommodation-accessibility decision tree tool. This DiP combines Shields's transformative, Heifetz's adaptive, and Ubuntu's socio-ethical leadership approaches to change. The implementation plan is guided by Deszca and Ingols' change path model (CPM), a communication plan with the alignment, voice, identification, and dialogue (AVID) framework, and a plan, do, study, act (PDSA) monitoring and evaluation plan. A knowledge mobilization plan further illustrates change implementation. The successful implementation will provide a systemic infrastructure for TC and, potentially, other nursing education programs.\nKeywords: mental health, nursing, competencies, accommodation, universal design for learning, disability
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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 | 0.040 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".