Experiences of stroke survivors and measurement of post stroke participation and activity across seasons—A mixed methods approach
Bibliographic record
Abstract
Participation and activity post stroke can be limited due to adverse weather conditions. This study aimed to: Quantify and compare summer and winter participation and activity, and explore how community dwelling people with stroke describe their feelings about their level of participation and activity by season. This embedded mixed-methods observational study took place in a city with weather extremes. Community dwelling individuals at least one year post-stroke, able to walk ≥50 metres +/- a walking aide were included. Evaluations and interviews occurred at participants' homes in two seasons: Reintegration to Normal living Index (RNL), Activities-specific Balance Confidence (ABC) and descriptive outcomes. Participants wore activity monitors for one week each season. Analysis included descriptive statistics, non-parametric tests and an inductive approach to content analysis. Thirteen individuals participated in quantitative evaluation with eight interviewed. Mean age 61.5 years, 62% female and mean 6.2 years post-stroke. No differences between winter-summer values of RNL, ABC, or activity monitor outcomes. However, participants felt they could do more and were more independent in summer. The winter conditions such as ice, snow, cold and wind restricted participation and limited activities. Nonetheless, many participants were active and participated despite the winter challenges by finding other ways to be active, and relying on social supports and personal motivation. The qualitative findings explained unexpected quantitative results. Participants described many challenges with winter weather, but also ways they had discovered to participate and be active despite these challenges. Changes to future studies into seasonal differences are suggested.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".