Experiences and factors affecting poverty among families raising a child with a disability: a scoping review
Bibliographic record
Abstract
PURPOSE: Children with disabilities and their families are more likely to live in poverty than those without a disability. However, little is known about their lived experiences and the circumstances that influence their socio-economic condition. The aim of this study was to understand the experiences and factors affecting poverty among families raising children with disabilities. METHODS: A scoping review was conducted while searching eight international databases (Embase, Healthstar, Medline, PsycINFO, Econlit, Scopus, Web of Science, and Sociological Abstracts). About 2351 articles were independently screened, and 48 studies met the inclusion criteria. RESULTS: The 48 studies included in the review involved 18 countries over a 26-year period. Our findings highlight the following key trends: (1) rates and extent of poverty, (2) types of poverty (i.e., material hardship, cost burdens, food insecurity), and (3) factors affecting poverty among children and youth with disabilities and their families (i.e., type and severity of disability, age, race/ethnicity, single-parent households, employment status, education, financial supports, type of health insurance, and community and societal factors). CONCLUSIONS: There is an urgent need for anti-poverty policies, interventions and resources to help support families raising a child with a disability.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".