Validation of the Rainbow Model of Integrated Care Measurement Tool in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Integrated care is essential for improving the management and health outcomes for people with Parkinson's disease (PD); reliable and objective measures of care integration are few. OBJECTIVE: The aim of this study was to test the psychometric properties of the Rainbow Model of Integrated Care Measurement Tool (RMIC-MT, provider version) for healthcare professionals involved in PD care. METHODS: A cross-sectional survey was administered online to an international network representing 95 neurology centers across 41 countries and 588 healthcare providers. Exploratory factor analysis with principal axis extraction method was used to assess construct validity. Confirmatory factor analysis was used to evaluate model fit of the RMIC-MT provider version. Cronbach's alpha was used to assess the internal consistency reliability. RESULTS: Overall, 371 care providers (62% response rate) participated in this study. No item had psychometric sensitivity problems. Nine factors (professional coordination, cultural competence, triple aims outcome, system coordination, clinical coordination, technical competence, community-centeredness, person-centeredness, and organizational coordination) with 42 items were determined by exploratory factor analysis. Cronbach's alpha ranged from 0.76 (clinical coordination) to 0.94 (system coordination) and showed significant correlation among all items in the scale (>0.4), indicating good internal consistency reliability. The confirmatory factor analysis model passed most goodness-of-fit tests, thereby confirming the factor structure of nine categories with a total of 40 items. CONCLUSIONS: The results provide evidence for the construct validity and other psychometric properties of the provider version of the RMIC-MT to measure integrated care in PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.030 | 0.067 |
| 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".