Factors impacting the access and use of formal health and social services by caregivers of stroke survivors: an interpretive description study
Bibliographic record
Abstract
BACKGROUND: Evidence has shown that family and friend caregivers of stroke survivors are significantly and negatively impacted by caregiving. The negative effects of caregiving may persist over time suggesting that caregivers might benefit from ongoing engagement with supportive services. However, little is known about caregivers' use of formally funded health and social services, or the factors influencing their access to and use of these services. The aim of this study is to increase understanding of the factors that influence stroke caregivers' access and use of formal health and social services, from the perspective of stroke caregivers and healthcare providers. METHODS: A qualitative study was conducted with stroke caregivers and health providers in Ontario, Canada using interpretive description. In-depth interviews were conducted with caregivers of survivors who experienced a stroke between six months to five years previous and healthcare providers who support caregivers and stroke survivors. All participants provided written informed consent. Interview data were analyzed using constant comparison to identify codes and develop key thematic constructs. RESULTS: A total of 40 interviews were conducted with 22 stroke caregivers at an average 30-months post-stroke and 18 health providers. Factors that influenced stroke caregivers' access and use of services included: finances and transportation; challenges caregivers faced in caring for their health; trust that they could leave their family member and trust in health providers; limited information pertaining to services and a lack of suitable services; and the response of their social networks to their caregiving situation. CONCLUSION: Stroke caregivers experience significant challenges in accessing and using formal health and social services. These challenges could be addressed by increasing availability of subsidized community-based supports such as respite and counselling tailored to meet the ongoing needs of caregivers. Systemic change is needed by the health system that readily includes and supports caregivers throughout the stroke recovery continuum, particularly in the community setting.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".