Psychologists' perspectives on the diagnostic classification of mental disorders: Results from the WHO‐IUPsyS Global Survey
Bibliographic record
Abstract
This study examined psychologists' views and practices regarding diagnostic classification systems for mental and behavioral disorders so as to inform the development of the ICD-11 by the World Health Organization (WHO). WHO and the International Union of Psychological Science (IUPsyS) conducted a multilingual survey of 2155 psychologists from 23 countries, recruited through their national psychological associations. Sixty percent of global psychologists routinely used a formal classification system, with ICD-10 used most frequently by 51% and DSM-IV by 44%. Psychologists viewed informing treatment decisions and facilitating communication as the most important purposes of classification, and preferred flexible diagnostic guidelines to strict criteria. Clinicians favorably evaluated most diagnostic categories, but identified a number of problematic diagnoses. Substantial percentages reported problems with crosscultural applicability and cultural bias, especially among psychologists outside the USA and Europe. Findings underscore the priority of clinical utility and professional and cultural differences in international psychology. Implications for ICD-11 development and dissemination are discussed.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".