Exploring Clinicians' Suggestions for Addressing Discrimination Towards Children and Youth With Disabilities With Multiple Minoritized Identities
Bibliographic record
Abstract
BACKGROUND: Clinicians can help address the discrimination that children and youth with disabilities often encounter. However, they commonly report lacking training and experience in addressing multiple forms of discrimination, such as ableism and racism. A lack of knowledge could lead to stigma and inequities within healthcare. This study explores clinicians' suggestions for addressing multiple forms of discrimination among children and youth with disabilities. METHODS: This qualitative study involved in-depth interviews with a purposive sample of 15 paediatric rehabilitation clinicians and community service providers working with disabled youth who have multiple minoritized identities. We applied an inductive thematic analysis to the interview transcripts. RESULTS: Our findings highlighted the following four themes: (1) disability awareness and anti-ableism training and education; (2) enhancing inclusive programming, services and policies; (3) building connections to supports and resources; and (4) advocacy and incorporating lived experience perspectives. CONCLUSION: The results underscore the need for clinicians to engage in more training and to facilitate access to resources for multiply minoritized youth with disabilities. Dedicated funding, resources and commitment at organizational, systems and policy levels are needed to address discrimination.
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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 | 0.038 | 0.065 |
| 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 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".