My MAPS: incorporating personal and social factors to increase physical activity after stroke: a co-design study
Bibliographic record
Abstract
PURPOSE: Engaging in meaningful social and community activities may be an option for promoting sustainable post-stroke physical activity participation. This research aimed to co-design an intervention to help people re-engage in meaningful activities and lead an active life after stroke. METHODS: A four-stage co-design methodology using an Integrated Knowledge Translation (IKT) approach was used to design a post-stroke intervention and resources: Stage one: research team planning, stage two: knowledge-user content development, stage three: research team intervention and resource development, stage four: knowledge-user review and research team adaptation and finalisation. The research team included four clinical researchers with expertise in stroke and/or physical activity, two stroke clinicians, and one stroke survivor. Knowledge-user informants were recruited to participate (Stages 2 and 4) and included eight stroke survivors (four female), median age 67 years (IQR 55-74); and 11 health professionals (10 allied-health and one registered nurse). RESULTS: An intervention and resources aiming to facilitate physical activity was successfully developed. It comprises a patient-facing booklet (including secondary-stroke prevention education, structured meaningful activity identification, and goal setting) and a health professional resource (including education, upskilling information, and resources). CONCLUSIONS: This study successfully co-designed an intervention and resources which are ready for feasibility and acceptability testing.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".