Helping chronically ill or disabled people into work: what can \nwe learn from international comparative analyses?
Bibliographic record
Abstract
This project has added to knowledge in five main areas: \nIt has mapped the range and types of policies and interventions that have been implemented in Canada, Denmark, Norway, Sweden and the UK that may influence \nemployment chances for chronically ill and disabled people. By doing so it has added to understanding about what has actually been tried in each country and what might be considered in others. \nIt has refined a typology of the focussed interventions that have been identified, based on the underlying programme logic of the intervention, which aids strategic thinking about national efforts to help chronically ill and disabled people into work. \nIt has produced systematic reviews of the impact of the focussed interventions on the employment chances of chronically ill and disabled people and demonstrated the use of the typology in helping to interpret the results of the evaluations. \nThe project’s empirical analyses of individual-level data have identified how chronically ill people from different socio-economic groups have fared in the labour markets of the five countries over the past two decades. It has then tested these findings against hypotheses about the impact of macro-level labour market policies on chronically ill people to provide insights into the influence of the policy context. \nThe project has contributed to methodological development in evidence synthesis and the evaluation of natural policy experiments. By studying a small number of countries in great depth, we gained greater understanding of the policies and interventions that have been tried in these countries to help chronically ill and disabled people into work, against the backdrop of the wider labour market and macro-economic trends in those countries. We then integrated evidence from the wider policy context into the findings of systematic reviews of effectiveness of interventions, to advance interpretation of the natural policy experiments that have been implemented in these countries.
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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 | 0.082 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".