Effectiveness of return-to-work interventions for disabled people: a systematic review of government initiatives focused on changing the behaviour of employers
Bibliographic record
Abstract
BACKGROUND: OECD countries over the past two decades have implemented a range of labour market integration initiatives to improve the employment chances of disabled and chronically ill individuals. This article presents a systematic review and evidence synthesis on effectiveness of government interventions to influence employers' employment practices concerning disabled and chronically ill individuals in five OECD countries. A separate paper reports on interventions to influence the behaviour of employees. METHODS: Electronic and grey literature searches to identify all empirical studies reporting employment effects and/or process evaluations of government policies aimed at changing the behaviour of employers conducted between 1990 and 2008 from Canada, Denmark, Norway, Sweden and the UK. RESULTS: Few studies provided robust evaluations of the programmes or their differential effects and selection of participants into programmes may distort the findings of even controlled studies. A population-level effect of legislation to combat discrimination by employers could not be detected. Workplace adjustments had positive impacts on employment, but low uptake. Financial incentives such as wage subsidies can work if they are sufficiently generous. Involving employers in return-to-work planning can reduce subsequent sick leave and be appreciated by employees, but this policy has not been taken up with the level of intensity that is likely to make a difference. Some interventions favour the more advantaged disabled people and those closer to the labour market. CONCLUSIONS: Future evaluations need to pay more attention to differential impact of interventions, degree of take-up, non-stigmatizing implementation and wider policy context in each country.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".