Stroke and Physical Activity WORKSHOP DESCRIPTION
Bibliographic record
Abstract
A 45-60 minute interactive workshop presenting best practice evidence on physical activity and stroke. A PowerPoint presentation and speaker notes have been developed. It is intended that the speakers adapt the content for local course participants and as evidence changes. However, Heart and Stroke Foundation logos, style guides and branding must not be altered or removed. For example, it is acceptable to add another hospital's logo, but the Heart and Stroke Foundation logo cannot be removed. References to the Ministry of Health and Long-Term Care or Ontario Stroke System must not be removed. Any acknowledgements that are part of the package must also stay in place. LEARNING OBJECTIVES • To describe stroke and its effect. • To describe how exercise contributes to the prevention of stroke. • To describe how activity and exercise is affected by stroke. • To present the most recent evidence on physical activity and exercise following stroke. • To present the exercise recommendations following stroke. • To introduce the concept of adaptive activity. • To emphasize the need for self-management in physical activity. • To relate concepts to the reduction in falls. TARGET AUDIENCE Healthcare providers involved in stroke prevention, Falls Coalition volunteers, recreational therapists, fitness/personal trainers, PSWs, home support workers.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.308 | 0.178 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".