Negotiating legitimacy and belonging: Disabled students' and practitioners' experience
Bibliographic record
Abstract
INTRODUCTION: People with disabilities are underrepresented in health professions education and practice. Barriers for inclusion include stigma, disabling discourses, discriminatory programme design and oppressive interactions. Current understandings of this topic remain descriptive and fragmented. Existing research often includes only one profession, excludes particular types of disability and focuses on one aspect of the career journey. To expand understanding, we examined the recurrent forms of social relations that underlie the participation of disabled individuals in learning and practice contexts across five health professions. METHOD: We analysed 124 interviews with 56 disabled health practitioners and students. Participants were interviewed up to three times over 1.5 years. Using constructivist grounded theory, authors used a staged analytic approach that resulted in higher level conceptual categories that advance interpretations of social processes. Finally, the authors compared and integrated findings among students and practitioners. RESULTS: Participants experience challenges to their sense of legitimacy and belonging as health providers. They describe tensions within the health education and practice between the commitment to inclusion and the day-to-day realities experienced by disabled participants. We identified six distinct, but related, conditions underlying these tensions: (i) validity and transparency of competencies' evaluation; (ii) the social and physical contexts; (iii) integration of inclusive practices; (iv) boundaries between personal and professional identities; (v) vulnerability to authority figures; and (vi) dynamic person-level factors. DISCUSSION: If we are to commit to health practitioners and students with disabilities experiencing an overall sense of legitimacy and belonging, priority needs to be given to system-level practices and policies to support inclusion. Attention to the day-to-day marginalisation of students and practitioners with disabilities in the health professions is also needed. Additionally, inclusive and transparent delineation of competency requirements is needed. Finally, educational actions are needed to increase understanding of disability in the health professions, with particular attention to promoting social relations that foster collective responsibility for supporting inclusion.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| 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".