Developing the World Health Organization Disability Assessment Schedule 2.0
Bibliographic record
Abstract
OBJECTIVE: To describe the development of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) for measuring functioning and disability in accordance with the International Classification of Functioning, Disability and Health. WHODAS 2.0 is a standard metric for ensuring scientific comparability across different populations. METHODS: A series of studies was carried out globally. Over 65,000 respondents drawn from the general population and from specific patient populations were interviewed by trained interviewers who applied the WHODAS 2.0 (with 36 items in its full version and 12 items in a shortened version). FINDINGS: The WHODAS 2.0 was found to have high internal consistency (Cronbach's alpha, α: 0.86), a stable factor structure; high test-retest reliability (intraclass correlation coefficient: 0.98); good concurrent validity in patient classification when compared with other recognized disability measurement instruments; conformity to Rasch scaling properties across populations, and good responsiveness (i.e. sensitivity to change). Effect sizes ranged from 0.44 to 1.38 for different health interventions targeting various health conditions. CONCLUSION: The WHODAS 2.0 meets the need for a robust instrument that can be easily administered to measure the impact of health conditions, monitor the effectiveness of interventions and estimate the burden of both mental and physical disorders across different populations.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".