Poverty alleviation policies, programs and practices for people with disabilities: A scoping review and recommendations
Bibliographic record
Abstract
BACKGROUND: People with disabilities have a higher prevalence of living in poverty compared to people without disabilities, which largely results from the challenges, barriers, inequalities and discrimination they often encounter. However, little is known about relevant policies, practices, and anti-poverty interventions that could facilitate a better quality of life for people with disabilities. METHODS: A scoping review following the Joanna Briggs Institute methodology was used to explore the existing practices, policies and interventions to address poverty among people with disabilities. The search involved six international databases: Ovid Medline, Healthstar, PsychINFO, Econlit, Scopus and Web of Science where two reviewers screened 4548 studies for inclusion. RESULTS: Thirty-seven studies were included in the review, which spanned across 20 countries. Our review noted the following key trends: (1) poverty alleviation policies; (2) programs and practices to address poverty (e.g., benefits, barriers and factors affecting access); and (3) cash transfers, especially their impact and factors affecting transfers. CONCLUSIONS: The findings of this review underscore the potential value of poverty alleviation strategies and policies for assisting people with disabilities. The results could help to inform guidelines and recommendations for policies, practices, and interventions to help alleviate poverty among people 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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".