Optimizing hospital-to-home transitional care to promote functioning of older persons with dementia in rural communities
Bibliographic record
Abstract
During and after a hospital stay, older people with dementia are at risk of losing the ability to function independently in everyday activities like getting up and dressing themselves. If they do not get back to functioning independently within one month of coming home, they are unlikely to ever do so. Transitional care refers to the healthcare services people receive to ensure their needs continue to be met as they move from hospital to home. Most transitional care studies excluded older people with dementia and their families and did not focus on helping people get back to functioning in everyday activities. In addition, the few studies that focused on functioning excluded rural communities. These exclusions have resulted in gaps in knowledge to guide practice and policy for this population. This study will address these gaps. We reviewed the literature and identified six interventions that are likely to be helpful. In this 3-year CIHR-funded study, we will invite older people with dementia who were discharged from hospital and their families in rural areas in Ontario and Nova Scotia to tell us what they think about the interventions. We will ask them if the interventions fit their needs and how the interventions can better fit their needs. Then, we will bring these interventions forward to healthcare providers who give transitional care in rural Ontario and Nova Scotia. We will invite the healthcare providers to tell us what they think about the interventions, what they would need to provide them, and how to enhance their delivery so that healthcare providers can deliver them. We will use the findings to refine the interventions so that they become part of transitional care in rural areas and help older people with dementia and their families learn how to promote functioning at home after a hospital stay. The findings will also be used to guide our future research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".