Empowering and Supporting Young Caregivers Interacting with the Healthcare System: Translating Survey Findings through Co-Design
Bibliographic record
Abstract
In Canada and worldwide, there is a growing awareness of young carers, or youth under 25 who provide significant and ongoing unpaid care to a loved one. Previous work by our team has identified that young carers interact with healthcare systems at various points of care and experience challenges with receiving limited information or communication from care teams, lacking acknowledgment of their caring role, balancing caregiving responsibilities with school/work, and some are tasked with making critical healthcare decisions with minimal support. Little is also known about how equipped healthcare providers who may encounter young carers are at recognizing and supporting young carers at the point of care. Drawing from a survey of Swiss professionals working with young carers, our national, cross-sectional survey of healthcare providers aims to understand the level of awareness providers have regarding young carers, their ability to recognize and support youth in caring roles as clinicians, and training/resources that are needed to help providers at point of care. Using integrated knowledge translation and experience-based co-design approaches, the survey findings will inform the development and refinement of tools to support young carers as they interact with the healthcare system. Bringing together young carers, community organizations that support caring youth, and healthcare providers, we will work to identify priority areas, as informed by survey findings and lived experiences, which will drive the co-design of support tools. The project at large, including survey content, recruitment, co-design and dissemination is advised upon by key knowledge users, including young carers and community organizations, and is co-led by a young carer. Our presentation aims to review preliminary project findings on the national survey of healthcare providers and the key areas identified through co-design where resources are needed to empower and support young caregivers interacting with the healthcare system. Interactions with the healthcare system can be complex for any patient and their families; however, by engaging with young carers and healthcare providers, we hope to learn about the most critical areas they need support in to more meaningfully and efficiently improve the health and well-being of young carers and the individuals they care for.
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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 | 0.225 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".