Attitudes towards evaluation of psychiatric disability claims: a survey of Swiss stakeholders
Bibliographic record
Abstract
QUESTIONS: In Switzerland, evaluation of work capacity in individuals with mental disorders has come under criticism. We surveyed stakeholders about their concerns and expectations of the current claim process. METHODS: We conducted a nationwide online survey among five stakeholder groups. We asked 37 questions addressing the claim process and the evaluation of work capacity, the maximum acceptable disagreement in judgments on work capacity, and its documentation. RESULTS: Response rate among 704 stakeholders (95 plaintiff lawyers, 285 treating psychiatrists, 129 expert psychiatrists evaluating work capacity, 64 social judges, 131 insurers) varied between 71% and 29%. Of the lawyers, 92% were dissatisfied with the current claim process, as were psychiatrists (73%) and experts (64%), whereas the majority of judges (72%) and insurers (81%) were satisfied. Stakeholders agreed in their concerns, such as the lack of a transparent relationship between the experts' findings and their conclusions regarding work capacity, medical evaluations inappropriately addressing legal issues, and the experts' delay in finalising the report. Findings mirror the characteristics that stakeholders consider important for an optimal work capacity evaluation. For a scenario where two experts evaluate the same claimant, stakeholders considered an inter-rater difference of 10%‒20% in work capacity at maximum acceptable. CONCLUSIONS: Plaintiff lawyers, treating psychiatrists and experts perceive major problems in work capacity evaluation of psychiatric claims whereas judges and insurers see the process more positively. Efforts to improve the process should include clarifying the basis on which judgments are made, restricting judgments to areas of expertise, and ensuring prompt submission of evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".