“I just kept asking and asking and there was nothing”: re-thinking community resources & supports for young adult stroke survivors
Bibliographic record
Abstract
PURPOSE: Stroke is often regarded as a disease of the elderly. However, 10-15% of strokes occur in people aged 18 to 50, and rates continue to rise. Young stroke survivors face unique challenges due to their occupational, family and personal commitments, which current stroke rehabilitation services may not fully address. Our qualitative study aimed to identify gaps in patient care and resources for young stroke survivors. We used these findings to develop recommendations to inform clinical care, healthcare system design, and health policy. METHODS: Using Interpretive Description, we conducted semi-structured interviews with 19 stroke survivors aged 18-55 living in British Columbia, Canada, to explore their experiences during stroke recovery and assess current gaps in support and resources. We applied broad-based coding and thematic analysis to the transcripts. RESULTS: Key themes included: (1) the need for longitudinal medical follow-up and information provision, (2) the need for psychological/psychiatric care, (3) the need to adapt community supports and resources to young survivors, and (4) the need to centralize and integrate community stroke services and resources. CONCLUSION: Young stroke survivors experience unique challenges and lack appropriate services and resources. Many of our findings may be representative of remediable gaps that persist nationally and internationally.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".