Evaluating the implementation of the Mayo-Portland Adaptability Inventory-4 (MPAI-4) in three rehabilitation settings in Quebec: a mixed-methods study protocol
Bibliographic record
Abstract
INTRODUCTION: Stroke is a leading cause of morbidity and mortality worldwide, placing an immense burden on patients and the health system. Timely access to rehabilitation services can improve stroke survivors' quality of life. The use of standardised outcome measures is endorsed for optimising patient rehabilitation outcomes and improving clinical decision-making. This project results from a provincially mandated recommendation to use the fourth version of the Mayo-Portland Adaptability Inventory (MPAI-4) to measure changes in social participation of stroke survivors and to maintain commitment to evidence-informed practices in stroke care. This protocol outlines the implementation process of the MPAI-4 for three rehabilitation centres. The objectives are to: (a) describe the context of MPAI-4 implementation; (b) determine clinical teams' readiness for change; (c) identify barriers and enablers to implementing the MPAI-4 and match the implementation strategies; (d) evaluate the MPAI-4 implementation outcomes including the degree of integration of the MPAI-4 into clinical practice and (e) explore participants' experiences using the MPAI-4. METHODS AND ANALYSIS: We will use a multiple case study design within an integrated knowledge translation (iKT) approach with active engagement from key informants. Each case is a rehabilitation centre implementing MPAI-4. We will collect data from clinicians and programme managers using mixed methods guided by several theoretical frameworks. Data sources include surveys, focus groups and patient charts. We will conduct descriptive, correlational and content analyses. Ultimately, we will analyse, integrate data from qualitative and quantitative components and report them within and across participating sites. Results will provide insights about iKT within stroke rehabilitation settings that could be applied to future research projects. ETHICS AND DISSEMINATION: The project received Institutional Review Board approval from the Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal. We will disseminate results in peer-reviewed publications and at local, national and international scientific conferences.
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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 | 0.040 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".