Supporting rehabilitation stakeholders in making service delivery decisions: a rapid review of multi-criteria decision analysis methods
Bibliographic record
Abstract
This review aimed to synthesize knowledge about multi-criteria decision analysis methods for supporting rehabilitation service design and delivery decisions, including: (1) describing the use of these methods within rehabilitation, (2) identifying decision types that can be supported by these methods, (3) describing client and family involvement, and (4) identifying implementation considerations. We conducted a rapid review in collaboration with a knowledge partner, searching four databases for peer-reviewed articles reporting primary research. We extracted relevant data from included studies and synthesized it descriptively and with conventional content analysis. We identified 717 records, of which 54 met inclusion criteria. Multi-criteria decision analysis methods were primarily used to understand the strength of clients’ and clinicians’ preferences (n = 44), and five focused on supporting decision making. Shared decision making with stakeholders was evident in only two studies. Clients and families were mostly engaged in data collection and sometimes in selecting the relevant criteria. Good practices for supporting external validity were inconsistently reported. Implementation considerations included managing cognitive complexity and offering authentic choices. Multi-criteria decision analysis methods are promising for better understanding client and family preferences and priorities across rehabilitation professions, contexts, and caseloads. Further work is required to use these methods in shared decision making, for which increased use of qualitative methods and stakeholder engagement is recommended. IMPLICATIONS FOR REHABILITATIONMulti-criteria decision analysis methods are promising for evidence-based, shared decision making for rehabilitation.However, most studies to date have focused on estimating stakeholder preferences, not supporting shared decision making.Cognitive complexity and modelling authentic and realistic decision choices are major barriers to implementation.Stakeholder-engagement and qualitative methods are recommended to address these barriers. Multi-criteria decision analysis methods are promising for evidence-based, shared decision making for rehabilitation. However, most studies to date have focused on estimating stakeholder preferences, not supporting shared decision making. Cognitive complexity and modelling authentic and realistic decision choices are major barriers to implementation. Stakeholder-engagement and qualitative methods are recommended to address these barriers.
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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 | 0.188 | 0.365 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".