From Caregiver to Care Partner: A View From The Other Side
Bibliographic record
Abstract
Background As the trend towards aging in place continues to grow, unpaid caregivers are facing challenges that include access to relevant and meaningful resources, systemic barriers to efficient two-way communication with healthcare and service organizations, navigating the healthcare system, time management, financial strain, and difficult family dynamics. Objective This project was designed to help Tyze Networks broaden their understanding of how unpaid caregivers, and supportive healthcare and service organizations, perceive the value of formal managed care coordination. Methods This project began with a comprehensive environmental scan of existing research, policies, pathways, and best practices. This was followed by an online survey targeted at Canadian and American healthcare and service organizations and unpaid caregivers of older adults. The online survey link was provided to potential participants through the Centre for Elder Research's (CER) data base, social media, and Tyze's user base. The research team then reached out to volunteers from the survey to conduct semi structured, one-on-one virtual interviews. Results Four key findings were found: 1) Communicating with healthcare and other professionals was ranked as the number one challenge for the survey participants; 2) A total of 66% of unpaid caregivers reported that they provided non-healthcare related support to the care recipient; 3) Over 70% of the respondents stated a dedicated application would help them manage all or most of the care coordination; 4) Carrying out the numerous responsibilities of providing care often significantly impacts the unpaid caregivers' well-being and self-care. Discussion As care partners, caregivers can help by sharing information, participating in aspects of care, and helping to make decisions. They can be spokespersons, advocates, and supporters, especially if care recipients are too ill and unable to do this for themselves. Conclusion The implementation of a formal mechanism to communicate and coordinate care with healthcare and service organizations has the potential to relieve many of the challenges faced by informal caregivers. For these providers, caregivers can provide invaluable timely information and facilitate coordination of care services.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".