Use of a structured functional evaluation process for independent medical evaluations of claimants presenting with disabling mental illness: rationale and design for a multi-center reliability study
Bibliographic record
Abstract
BACKGROUND: Work capacity evaluations by independent medical experts are widely used to inform insurers whether injured or ill workers are capable of engaging in competitive employment. In many countries, evaluation processes lack a clearly structured approach, standardized instruments, and an explicit focus on claimants' functional abilities. Evaluation of subjective complaints, such as mental illness, present additional challenges in the determination of work capacity. We have therefore developed a process for functional evaluation of claimants with mental disorders which complements usual psychiatric evaluation. Here we report the design of a study to measure the reliability of our approach in determining work capacity among patients with mental illness applying for disability benefits. METHODS/DESIGN: We will conduct a multi-center reliability study, in which 20 psychiatrists trained in our functional evaluation process will assess 30 claimants presenting with mental illness for eligibility to receive disability benefits [Reliability of Functional Evaluation in Psychiatry, RELY-study]. The functional evaluation process entails a five-step structured interview and a reporting instrument (Instrument of Functional Assessment in Psychiatry [IFAP]) to document the severity of work-related functional limitations. We will videotape all evaluations which will be viewed by three psychiatrists who will independently rate claimants' functional limitations. Our primary outcome measure is the evaluation of claimant's work capacity as a percentage (0 to 100 %), and our secondary outcomes are the 12 mental functions and 13 functional capacities assessed by the IFAP-instrument. Inter-rater reliability of four psychiatric experts will be explored using multilevel models to estimate the intraclass correlation coefficient (ICC). Additional analyses include subgroups according to mental disorder, the typicality of claimants, and claimant perceived fairness of the assessment process. DISCUSSION: We hypothesize that a structured functional approach will show moderate reliability (ICC ≥ 0.6) of psychiatric evaluation of work capacity. Enrollment of actual claimants with mental disorders referred for evaluation by disability/accident insurers will increase the external validity of our findings. Finding moderate levels of reliability, we will continue with a randomized trial to test the reliability of a structured functional approach versus evaluation-as-usual.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".