Integrating critical disability perspectives in nursing education
Bibliographic record
Abstract
Undergraduate nursing education has a duty to make certain that the focus of both nursing practice with disabled people and nursing education are enabling, rather than disabling. However, depictions of disability in nursing education have been identified as inadequate and at times problematic, with insufficient attention paid to disability in curricula. In this paper, we provide an overview of representations of disability in nursing and examine the gaps and inadequacies in nursing education. We also support the argument that nursing educators must utilize critical perspectives on disability to challenge discrimination and address the gaps that currently exist. Finally, we focus on how nursing programs and educators can take action to support all nursing students to develop the knowledge, attitudes, and behaviours to meet the needs of disabled people in a more comprehensive and meaningful way. Practical and effective strategies are shared.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".