What Are We Talking About When We Talk About Spread of Brain Health Interventions: Improving Life in Rugged Landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it