Insights Into Roles for Health-Care Professionals in Meeting the Needs of Older Adults and Unpaid Caregivers During Health-Care Transitions
Bibliographic record
Abstract
We provided insights from older adults, their unpaid caregivers, and health-care professionals into specific roles for professionals within the health system to better meet the needs of community-dwelling older adults and their unpaid caregivers experiencing transitions between health services. We used a qualitative approach to collect data within one Canadian province from older adults and unpaid caregivers of older adults who participated in focus groups ( n = 98) and professionals working in the health system who participated in an online survey ( n = 52). Questions included experiences with health service transitions, strengths, challenges, and suggestions to improve transitions. Thematic analysis resulted in identifying seven specific roles for professionals in supporting health-care transitions: information and education, planning for future health needs, supporting the acceptance of necessary care, facilitating access to the right services at the right time, facilitating communication between services, facilitating the discharge planning process and advocacy for older adults and unpaid caregivers. Our results based on evidence from older adults, unpaid caregivers, and health-care professionals will inform future research and further development of the instrumental and relational roles for professionals supporting older adults and their caregivers experiencing health-care transitions.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".