A case-controlled field study evaluating ICD-11 proposals for relational problems and intimate partner violence
Bibliographic record
Abstract
Background/Objective: Intimate partner relationship problems and intimate partner abuse and neglect — referred to in this paper as “relational problems and maltreatment” — have substantial and well-documented impact on both physical and mental health. However, classification guidelines, such as those found in the International Classification of Diseases (ICD-10), are vague and unlikely to support consistent application. Revised guidelines proposed for ICD-11 are much more operationalized. We used standardized clinical vignette conditions with an international panel of clinicians to test if ICD-11 changes resulted in improved classification accuracy. Method: English-speaking mental health professionals (N = 738) from 65 nations applied ICD-10 or ICD-11 (proposed) guidelines with experimentally manipulated case presentations of presence or absence of (a) individual mental health diagnoses and (b) relational problems or maltreatment. Results: ICD-11, compared with ICD-10, guidelines resulted in significantly better classification accuracy, although only in the presence of co-morbid mental health problems. Clinician factors (e. g., gender, language, world region) largely did not impact classification performance. Conclusions: Despite being considerably more explicated, raters’ performance with ICD-11 guidelines reveals training issues that should be addressed prior to the release of ICD-11 in 2018 (e. g., overriding the guidelines with pre-existing archetypes for relationship problems and physical and psychological abuse). Antecedentes/Objetivo: Los problemas en la relación de pareja y relacionados con abuso y negligencia de pareja, referidos como “problemas relacionales y maltrato”, tienen un importante impacto en la salud física y mental. Sin embargo, guías de clasificación, como la Clasificación Internacional de Enfermedades (CIE-10), son vagas y su aplicación es inconsistente. Las guías propuestas por el CIE-11 son más operacionales. Junto con un panel de clínicos, utilizamos viñetas clínicas estandarizadas, para evaluar si los cambios propuestos por CIE-11 mejoraban la precisión de la clasificación. Método: Profesionales de la salud de habla inglesa (N=738) de 65 naciones compararon la aplicación del CIE-10 y CIE-11 en casos experimentales, estableciendo presencia o ausencia de (a) diagnósticos individuales de salud mental y (b) problemas de relaciones o maltrato. Resultados: CIE-11 tuvo resultados significativamente más precisos, aunque solo en presencia de comorbilidades de salud mental. Factores como género, idioma y región no presentaron mayor alteración. Conclusiones: Aunque el CIE-11 está mejor explicado, este estudio revela problemas de capacitación que deberían abordarse antes de su publicación en 2018.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".