Twelve principles to support caregiver engagement in health care systems and health research
Bibliographic record
Abstract
Family and friend caregivers (i.e., unpaid carers) play a critical role in meeting the needs of people across various ages and illness circumstances. Caregiver experiences and expertise, which are currently overlooked, should be considered in practice (such as designing and evaluating services) and when designing and conducting research. In order to improve the quality of health care we need to understand how best to meaningfully engage caregivers in research, policy and program development to fill this important gap. Our study aimed to determine principles to support caregiver engagement in practice and research. A pan Canadian meeting brought together 48 stakeholders from research, policy and practice and lived experience (caregivers) to share perspectives on caregiver engagement and co-design. Several presentations from each stakeholder group were shared, followed by discussion and report back sessions. Extensive notes were taken and members of the research team synthesized the findings into categories and presented them back to participants for verification. 12 core principles to support caregiver engagement in practice and research were identified and validated by attendees: use policy levers and incentives, make blunt structural changes, face fears, recognize caregivers and increase opportunities to engage, define what quality means, be mindful of whose experience is being represented, address language and power, engage early, clarify roles and expectations, listen and act on what you hear, measure, and create a community of learning. These principles provide a foundation to guide curriculum development, core competency training, future research and quality improvement activities in health care settings.
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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 | 0.308 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".