Types and factors affecting and impact of ableism among Asian children and youth with disabilities and their caregivers: a systematic review of quantitative studies
Bibliographic record
Abstract
PURPOSE: Asian children and youth with disabilities often experience multiple barriers and discrimination in education, healthcare, and social settings, which influence their well-being, especially the transition to adulthood. This review aims to explore the types, factors affecting and impact of ableism on Asian children and youth with disabilities and their caregivers. METHODS: We conducted a systematic review and a narrative synthesis whereby we searched the literature from six international databases, including Healthstar, Ovid Medline, Embase, PsycInfo, Scopus, and Web of Science. RESULTS: Twenty-nine studies were included in the review, and three themes were identified that related to ableism: (1) types and rates of ableism (i.e., stigma, bullying and victimization, and discrimination and inequalities); (2) factors affecting ableism (i.e. sociodemographic factors, familial factors, and societal factors); and (3) impacts of ableism (i.e. mental health, family impacts, and societal impacts). CONCLUSIONS: Our review highlights that ableism has various types and can be influenced by multiple factors, influencing social and health outcomes of Asian families with children and youth with disabilities. This review also emphasizes the importance of increasing the public's awareness regarding disabilities to reduce ableism among Asian families with children with disabilities.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".