A photovoice study exploring post-stroke life, joys, hopes, goals, and challenges identified by racialized individuals
Bibliographic record
Abstract
PURPOSE: Racialized individuals are underrepresented in stroke research despite reporting unmet recovery needs. Understanding self-management experiences can inform service delivery improvements. In this study, we aimed to explore experiences of stroke self-management and recovery. MATERIALS AND METHODS: Racialized adults (residing in Canada, self-identifying as non-White or Indigenous) living with stroke and caregivers participated. Photovoice methods involved: 1) initial interview; 2) participants' photographs illustrating important, joyful, and challenging aspects after stroke, strategies for overcoming challenges, and hopes/goals for recovery; 3) individual interview; and 4) focus group. Data were analyzed using thematic analysis. RESULTS: Fourteen individuals living with stroke and five caregivers participated. Themes highlighted the joy in daily life from cherished relationships and food, mealtimes, and preparing meals. Participants navigated personal and cultural factors in stroke self-management and recovery, and their recovery hopes and goals included maximizing independence and resuming valued activities. CONCLUSIONS: These findings capture the experiences of racialized participants and reinforce the need for individualized and culturally appropriate stroke services to address ongoing challenges. Our findings highlight the importance of understanding what is meaningful after a stroke and how to support engagement in activities and roles that are most important to each person at that particular recovery stage.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".