Active labour market policies for people with disabilities in receipt of public income benefits: A scoping review
Bibliographic record
Abstract
BACKGROUND: Active labour market policies (ALMP) are used in advanced welfare states to support transitions to work for people who are unemployed or underemployed, including people with disabilities (PWD) in receipt of means-tested disability income support. OBJECTIVE: This study explores the nature, strength, and limitations of ALMP across advanced welfare states (ALMP) for people with disabilities (PWD) in receipt of income benefits from social assistance programs. METHODS: Following the eight steps of a scoping study, we identified 21 documents through a scan of eight databases and consultation with key informants. The majority of these documents are scholarly publications including seven literature reviews, two program evaluations, four social policy analyses, and two longitudinal studies. RESULTS: We extracted key findings related to delivery of labour (re)entry interventions for people with disabilities. Six themes are identified that discuss these ALMP features: 1) welfare ideology and the role of citizenship; 2) conditionality of benefits; 3) work capacity and the need for an appropriate definition of disability; 4) the politics of employment outcomes for PWD; 5) the missing elements of a successful ALMP; and 6) moving beyond ALMP. The findings indicate that while various approaches are used in reintegrating PWD into mainstream employment, there are significant limitations that curtail the impact of these policies. CONCLUSIONS: Regardless of welfare regime, no welfare state provides a policy mix that results in long-term employment success for PWD in receipt of means-tested income benefits.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".