Community-involved care transition interventions to support essential care partners of stroke survivors: a rapid review protocol
Bibliographic record
Abstract
Background Essential care partners (ECPs), also known as family caregivers, play a critical role in the Canadian healthcare system across the continuum of care, particularly in managing complex conditions like stroke. With the rising number of stroke incidents occurring in Canada each year, there is an increased need for caregiver assistance to help manage the care needs of stroke survivors as they transition to home and community services. Although existing research has highlighted the practical and psychosocial needs of stroke ECPs, these challenges have been mainly overlooked. The lack of integrated intersectoral care services across stroke care pathways places additional significant burdens on caregivers, leading to increased stress, social isolation and a decreased quality of life. Nelson and colleagues’ novel Discharge Assistance and Supports at Home model uses community-based interventions mobilised through intersectoral partnerships and volunteers as human resources to facilitate grassroots solutions to the discharge and transition challenges often faced by stroke survivors. As an extension of this work, this rapid review will investigate and detail community-involved or community-led interventions that have been proven effective in addressing the unmet needs of stroke ECPs during critical care transitions. The findings of this review will identify what works, for whom and in what context regarding community-involved caregiver-centred transition interventions to inform the creation of an actionable Research Agenda—DASH-Caregiver. Methods This rapid review will be conducted using the updated guidance on methods used in Cochrane rapid reviews of effectiveness. The search strategy will be refined by the study team with assistance from an information specialist and applied to six databases: Medline, Cochrane, Embase, CINAHL and PubMed. Grey literature will be searched using Google search engines, targeted websites and consultation with knowledge holders. Two research team members will conduct a two-stage screening process to determine study eligibility. Data from eligible studies will be extracted using a piloted charting form and synthesised narratively. Ethics and dissemination This review protocol does not require ethics approval, as no data have been collected or analysed. The results will be shared with key knowledge holders through publications and presentations and incorporated into the team’s future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.012 |
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".