Co-Designing Caregiver Support Groups: Rapid Review with a Non-Profit Organization
Bibliographic record
Abstract
This rapid review aims to synthesize literature on co-designing support groups for caregivers of older adults, focusing on how caregivers are involved in the co-design process. This synthesis is undertaken in collaboration with the Saskatoon Council on Aging to inform the development of caregiver support groups. Guided by the Cochrane Rapid Reviews Methods Group, this review follows a streamlined evidence synthesis approach. Our search strategy was run on June 5, 2023, on the MEDLINE, EMBASE, CINAHL, Cochrane CENTRAL, and PsycINFO databases. Three studies met the inclusion criteria, highlighting the importance of comprehensive initiatives and peer support. Key findings include the necessity of tailoring support groups to caregivers’ specific needs and preferences, the combination of online and offline support, and the importance of cultural sensitivity in service design. Methodological approaches to co-design varied across studies, with each emphasizing the importance of iterative feedback loops and engagement with diverse caregiver groups. The review underscores the value of co-designed support groups in effectively meeting caregivers’ needs. Engaging caregivers in the design process ensures that support groups are tailored to their requirements and empowers them to shape their support systems. The insights from this review can guide future co-design efforts, enhancing caregiver support initiatives.
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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 | 0.064 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".