Standardization of health outcomes assessment for depression and anxiety: recommendations from the ICHOM Depression and Anxiety Working Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: National initiatives, such as the UK Improving Access to Psychological Therapies program (IAPT), demonstrate the feasibility of conducting empirical mental health assessments on a large scale, and similar initiatives exist in other countries. However, there is a lack of international consensus on which outcome domains are most salient to monitor treatment progress and how they should be measured. The aim of this project was to propose (1) an essential set of outcome domains relevant across countries and cultures, (2) a set of easily accessible patient-reported instruments, and (3) a psychometric approach to make scores from different instruments comparable. METHODS: Twenty-four experts, including ten health outcomes researchers, ten clinical experts from all continents, two patient advocates, and two ICHOM coordinators worked for seven months in a consensus building exercise to develop recommendations based on existing evidence using a structured consensus-driven modified Delphi technique. RESULTS: The group proposes to combine an assessment of potential outcome predictors at baseline (47 items: demographics, functional, clinical status, etc.), with repeated assessments of disease-specific symptoms during the treatment process (19 items: symptoms, side effects, etc.), and a comprehensive annual assessment of broader treatment outcomes (45 items: remission, absenteeism, etc.). Further, it is suggested reporting disease-specific symptoms for depression and anxiety on a standardized metric to increase comparability with other legacy instruments. All recommended instruments are provided online ( www.ichom.org ). CONCLUSION: An international standard of health outcomes assessment has the potential to improve clinical decision making, enhance health care for the benefit of patients, and facilitate scientific knowledge.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it