Influence of low income on return to participation following stroke
Bibliographic record
Abstract
Purpose: Low income is known to influence participation post stroke, but the process by which this occurs is poorly understood.Methods: A qualitative multiple case study approach, focusing on the experience of returning to participation in personal projects among eight low-income francophone stroke survivors living in eastern Ontario (Canada). Data included semi-structured interviews with the stroke survivors and with their care partners, participant observations, assessment measures, and chart reviews.Results: Healthcare professionals inconsistently gave the stroke survivors needed information and assistance to access entitlements during discharge planning. Income support programs were difficult to access and once obtained, were not completely adequate to support essential necessities (food, medication) in addition to other goods and services related to valued activities. Housing was an important monthly expense that limited available monies for personal projects. Only in instances where participants were assisted with housing by informal networks were there adequate funds to pursue personal projects.Conclusion: This case study demonstrated that even in a universal healthcare system, post-stroke participation for those with low incomes was severely restricted. Changes at the clinical level and at the public policy level could facilitate participation.Implications for rehabilitationIncome influenced the experience of return to participation for the low-income stroke survivors by limiting their ability to afford housing, goods, and services.The macro environment, which regulates the healthcare and social service systems, was the strongest influence on return to participation for low income stroke survivors.Findings point to actions at the clinical and policy levels to help address this inequity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".