Optimizing Measurement Potential in Mental Health Clinical Practice: The Canadian Personal Recovery Outcome Measure (C-PROM) study
Bibliographic record
Abstract
Abstract There is an increasing commitment to “Personal Recovery” as the desired outcome for mental health rehabilitation, yet there is little agreement about how to measure it. The purpose of this study was to develop a method of measuring recovery in community-dwelling people with mental health concerns. We describe a four-phase process, informed by guidelines for (patient-reported outcome measure) PROM development described by the Scientific Advisory Committee of the Medical Outcomes Trust, the Food and Drug Administration, and the International Society for Quality of Life Research, on how to quantify recovery in mental health care. The approach includes an iterative mixed methods process, guided by Classical Test Theory (CTT) and Rasch Measurement Theory (RMT), to develop the Canadian Personal Recovery Outcome Measure (C-PROM) for adults receiving mental health services. In Phases 1 and 2, 40 new items were generated by people with schizophrenia. In Phase 3, psychometric analysis and cognitive interviewing suggested that the item set be reduced to 30 items. Phase 4 (n = 575) showed good overall fit of the C-PROM items to the Rasch model (χ 2 = 163, df = 130, p = .05), no item misfit, high reliability ( r p = 0.92), an ordered response scale structure, high correlation between logit and scale scores (0.92) and no item bias for gender, age, or diagnosis. This study provided evidence for the C-PROM as a measure of personal recovery for people with mental health concerns. The measurement model underpinning this set of items has potential to support clinical relevance of scale scores, advancing an evidence-based approach to mental health rehabilitation practice and outcomes.
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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 | 0.093 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".