Adapting Lifestyle Redesign <sup>®</sup> : Navigating Fidelity and Contextual Fit Across Four Case Examples
Bibliographic record
Abstract
Background. Lifestyle Redesign ® occupational therapy (LR-OT) originated in the Well Elderly studies as a preventive intervention for older adults, demonstrating positive health and cost outcomes. Although LR-OT later expanded to chronic condition management and inspired numerous programme adaptations for different contexts and populations, systematic reporting of intervention modifications remains scarce. This gap extends beyond OT, reflecting a broader movement in implementation science. Purpose. This study applies the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) to systematically document adaptations across four LR-OT programmes. Additionally, we examine fidelity-consistency by mapping programme components to the Well Elderly programme and core LR-OT elements. Methods. Four teams implementing adapted programmes—diabetic foot ulcer self-management, primary care chronic condition management, Remodeler sa Vie for French-Canadian older adults, and LR weight management—participated in a structured mapping exercise to align programmes with FRAME and LR-OT principle components. Results. Adaptation approaches varied from highly structured to loosely standardized methods. Despite numerous modifications, all programmes strongly aligned with LR-OT's core characteristics and domains. Conclusion. This study highlights LR-OT's adaptability across diverse contexts while maintaining fidelity to its foundational framework. Findings contribute to implementation research, providing a model for systematically documenting and characterizing adaptations made to client-centred, evidence-based OT programmes.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".