The impact of ableism on the health and well-being of children and youth with disabilities: a systematic review
Bibliographic record
Abstract
PURPOSE: Discrimination can affect both physical and mental health. Most research focuses on the health impacts of general discrimination or racism, while researchers give less attention to disability-related discrimination (ableism). Children and youth with disabilities are especially vulnerable to ableism. Our review aimed to understand the impact of ableism on the health of children and youth with disabilities. METHODS: A systematic review was conducted involving eight international databases (Ovid Medline, Healthstar, PsychInfo, CINAHL, JBI EBP Database, Sociological Abstracts, Scopus, Web of Science). Four researchers independently screened 4698 articles for inclusion. RESULTS: The thirty included studies represented 20,495 children and youth with disabilities and parents representing them across 12 countries. The findings show that ableism negatively affects the physical health (i.e., physical injury and pain; worsening disability symptoms), mental health (i.e., overall psychological health; distress and emotional problems; depression; anxiety; suicidality; self-confidence, self-perception, and outlook), and overall quality of life among children and youth with disabilities. CONCLUSION: Researchers must urgently address how ableism contributes to negative physical and mental health outcomes and poorer quality of life for children and youth with disabilities. Research should continue exploring how ableism, including organizational forms of it, affects the health of youth.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".