Psychometric validation of a multi-dimensional capability instrument for outcome measurement in mental health research (OxCAP-MH)
Bibliographic record
Abstract
BACKGROUND: Patient reported outcome measures (PROMs) are widely used in mental healthcare research for quality of life assessment but most fail to capture the breadth of health and non-health domains that can be impacted. We report the psychometric validation of a novel, multi-dimensional instrument based on Amartya Sen's capability approach intended for use as an outcome measure in mental health research. METHODS: The Oxford Capabilities Questionnaire for Mental Health (OxCAP-MH) is a 16-item self-complete capability measure that covers multiple domains of functioning and welfare. Data for validation of the instrument were collected through a national randomised controlled trial of community treatment orders for patients with psychosis. Complete OxCAP-MH data were available for 172 participants. Internal consistency was established with Cronbach's alpha; an interclass correlation coefficient was used to assess test-retest reliability in a sub-sample (N = 50) tested one week apart. Construct validity was established by comparing OxCAP-MH total scores with established instruments of illness severity and functioning: EuroQol (EQ-5D), Brief Psychiatric Rating Scale (BPRS), Global Assessment of Functioning (GAF) and Objective Social Outcomes Index (SIX). Sensitivity was established by calculating standard error of measurement using distributional methods. RESULTS: The OxCAP-MH showed good internal consistency (Cronbach's alpha 0.79) and test-retest reliability (ICC = 0.86). Convergent validity was evidenced by strong correlations with the EQ-5D (VAS 0.52, p < .001) (Utility 0.45, p < .001), and divergent validity through more modest associations with the BPRS (-0.41, p < .001), GAF (0.24, p < .001) and SIX (0.12, p = ns). A change of 9.2 points on a 0-100 scale was found to be meaningful on statistical grounds. CONCLUSIONS: The OxCAP-MH has demonstrable reliability and construct validity and represents a promising multi-dimensional alternative to existing patient-reported outcome measures for quality of life used in mental health research.
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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 | 0.047 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".