What Are We Talking About When We Talk About Spread of Brain Health Interventions: Improving Life in Rugged Landscapes
Bibliographic record
Abstract
This paper investigates the challenges and strategies for expanding Dancing with Parkinson’s (DWP)—a Toronto-based dance intervention for Parkinson’s disease—into Indigenous communities in Northern Ontario. Using implementation science frameworks and a case study approach, and informed by a realist evaluation lens, the study defines spread as the replication of core program components in new settings through contextual adaptation. Key challenges in replicating interventions across diverse environments include: Specific barriers to the spread of Dancing with Parkinson’s discussed include unreliable internet access, cultural misalignment with Western-centric dance practices, and historical distrust of externally imposed healthcare initiatives. The analysis argues that successful spread requires prioritizing cultural adaptiveness and developing a “choice infrastructure” (e.g., broadband access, Indigenous-led partnerships). The chapter critiques linear replication models, advocating instead for dynamic, systems-oriented approaches that emphasize: community agency, iterative learning processes, and realist evaluation principles to guide adaptation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".