Supporting post-stroke access to services and resources for individuals with low income: understanding usual care practices in acute care and rehabilitation settings
Bibliographic record
Abstract
PURPOSE: Following stroke, individuals who live in a low-income or are at risk of living in a low-income situation face challenges with timely access to social services and community resources. Understanding the usual care practices of stroke teams, specifically, how they support this access to services and resources, is an important first step in promoting the implementation of practice change. METHOD: A qualitative multiple-case study of acute care, inpatient, and outpatient rehabilitation stroke teams in an urban area of Canada. Semi-structured interviews and questionnaires about the workplace context were conducted with 19 professionals (social workers, occupational therapists, physiotherapists, speech-language pathologists) at four sites. RESULTS: In their usual practice, stroke teams prioritized immediate care needs. The stroke team professionals did not address income or resources unless it directly affected discharge. Usual care was influenced by factors such as time constraints, lack of knowledge about services and resources, and social service system limitations. CONCLUSION: To better support post-stroke access to social services and resource for low-income individuals, a multidisciplinary approach, with actions beginning earlier on and extending throughout the continuum of care, is recommended, in addition to system-level advocacy.
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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 | 0.004 | 0.009 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".