Distrusting doctors’ evidence: a qualitative study of disability income support policy makers in Australia and Ontario, Canada
Bibliographic record
Abstract
Objective The aim of the present study was to describe how policy makers (bureaucrats and politicians) in Australia and Ontario (Canada) perceive evidence provided by doctors to substantiate applications for disability income support (DIS) by their patients with mental illnesses. Because many mental illnesses (e.g. depression) lack diagnostic tests, their existence and effects are more difficult to demonstrate than most somatic illnesses. Methods Semi-structured interviews were conducted with 45 informants, all influential in the design of the assessment of DIS programs. The informants were subcategorised into advocates, legal representatives, doctors (general practitioners (GPs) and specialists (e.g. psychiatrists)), policy insiders and researchers. Informants were found through snowball sampling. Following the principles of grounded theory, data collection and analysis occurred in tandem. Results Informants expressed some scepticism about doctors' evidence. Informants perceived that doctors could, due to lack of diagnostic certainty, 'write these things [evidence] however [they] want to'. Psychiatrists, perceived as having more time and skills, were considered as providing more trustworthy evidence than GPs. Conclusion Doctors, providing evidence to support applications, play an important role in determining disability. However, policy makers perceive doctors' evidence about mental illnesses as less trustworthy than evidence about somatic illnesses. This affects decisions by government adjudicators. What is known about the topic? Doctors (GPs and psychiatrists) are often asked to provide evidence to substantiate a DIS application for those with mental illnesses. We know little about the perception of this evidence by the policy makers who consider these applications. What does this paper add? Policy makers distrust doctors' evidence in relation to mental illnesses. This is partly because many mental illnesses lack diagnostic proof, in contrast with evidence for somatic conditions, where the disability is often visible and proven through diagnostic tests. Furthermore, GPs' evidence is considered less trustworthy than that of psychiatrists. What are the implications for practitioners? Although doctors' evidence is often required, the utility of their evidence is limited by policy makers' perceptions.
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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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".