Formal Health and Social Services That Directly and Indirectly Benefit Stroke Caregivers: A Scoping Review of Access and Use
Bibliographic record
Abstract
Stroke can be a life altering event that necessitates considerable amounts of formal and informal care. The impacts of stroke often persist over time requiring ongoing support for stroke survivors. Family members provide the majority of care and experience many life changes as a result of their caregiving role including social, financial, employment and health impacts. Formal supports such as counselling, respite, and health promotion initiatives that directly benefit caregivers or benefit them indirectly through supporting the stroke survivor, are well-placed to help caregivers manage their caregiving role. However, to date little is known about formal service use by stroke caregivers and the factors that influence their service use. This scoping review provides a critique and synthesis of what is known about stroke caregivers' access and use of formal services intended to support them. Findings suggest that while services are available, caregivers' ability to use them are impacted by both facilitators and barriers. Facilitators included: sex, age, and having a higher household income (depending on services used). Barriers included: high cost, poor service quality and deficient knowledge/communication regarding service availability. This review highlights a significant gap in our knowledge of caregivers' experience in accessing and using formal services.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".