The experiences and needs of Chinese-Canadian stroke survivors and family caregivers as they re-integrate into the community
Bibliographic record
Abstract
Stroke is a leading cause of adult disability and community re-integration is a priority for stroke rehabilitation. In North America, we have a growing population of individuals whose first language is not English. Little is known about the experiences of visible minorities living in North America as they re-integrate into the community post stroke or how these experiences change over time. Specifically, this research aimed to explore the experiences and needs of Chinese stroke survivors and family caregivers as they return to community living using the Timing it Right Framework as a conceptual guide. We recruited Cantonese-speaking stroke survivors and family caregivers from outpatient rehabilitation programmes. Using qualitative interviews conducted in Cantonese or English, we examined their experiences and needs as they return to community living and explored the influence of culture and time on their experiences. The interviews were transcribed and translated, and then analysed using framework analysis. Using framework analysis, we coded the data corresponding to the phases of the Timing it Right framework to determine the influence of time on the themes. We interviewed five Cantonese-speaking stroke survivors and 13 caregivers in 2009. We identified two main themes: (i) Participants' education and support needs change over time and (ii) Chinese resources are needed across care environments. These resources include access to care in their preferred language, traditional Chinese medicine, and Chinese food during their recovery and rehabilitation. To optimise Chinese stroke survivors' and caregivers' community re-integration, healthcare professionals should provide timely and accessible education and be aware of the role of Chinese diet and traditional medicine in stroke survivors' rehabilitation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".