Disability income support design and mental illnesses: a review of Australia and Ontario
Bibliographic record
Abstract
AIM: Mental illnesses have many distinctive features that make determining eligibility for disability income support challenging - for example, their fluctuating nature, invisibility and lack of diagnostic clarity. How do policy makers deal with these features when designing disability income support? More specifically, how do mental illnesses come to be considered eligible disabilities, what tools are used to assess mental illnesses for eligibility, what challenges exist in this process, and what approaches are used to address these challenges? We aimed to determine what evidence is available to policy makers in Australia and Ontario, Canada, to answer these questions. METHODS: Ten electronic databases and grey literature in both jurisdictions were searched using key words, including disability income support, disability pension, mental illness, mental disability, addiction, depression and schizophrenia, for articles published between 1991 and June 2013. This yielded 1341 articles, of which 20 met the inclusion criteria and were critically appraised. RESULTS: Limited evidence is available on disability income support design and mental illnesses in the Australian and Ontarian settings. Most of the evidence is from the grey literature and draws on case law. Many documents reviewed argued that current policy in Australia and Ontario is frequently based on negative assumptions about mental illnesses rather than evidence (either peer reviewed or in the grey literature). Problems relating to mental illnesses largely relate to interpretation of the definition of mental illness rather than the definition itself. CONCLUSIONS: The review confirmed that mental illnesses present many challenges when designing disability income support and that academic as well as grey literature, especially case law, provides insight into these challenges. More research is needed to address these challenges, and more evidence could lead to policies for those with mental illnesses that are well informed and do not reinforce societal prejudices.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".