SUPPORTING OPTIMAL AGING OF OLDER PERSONS WITH MULTIPLE CHRONIC CONDITIONS AND THEIR FAMILIES
Bibliographic record
Abstract
The prevalence of multiple chronic conditions (MCC) among older persons is increasing worldwide and is associated with poor health status and high rates of healthcare utilization and associated costs. Current health and social services are not addressing the complex needs of this group or their family caregivers who are largely responsible for their care in the community. There is uncertainty in the literature on the effectiveness of interventions for individuals who have MCC. The purpose of this symposium is to describe four studies, funded by the Canadian Institutes of Health Research Signature Initiative on Community Based Primary Healthcare, that contribute to our understanding of future directions for interventions to support optimal aging of older persons with MCC and their families. The first paper describes the results of a qualitative study of the experiences of managing MCC from the perspectives of 130 older adults with MCC, their caregivers and healthcare providers. The second study examined the patterns of health services use and associated costs among older adults with MCC using multiple linked administrative databases. The results of these two studies informed the design of two intervention studies. The third paper describes the results of a pragmatic randomized controlled trial of an interprofessional community-based health promotion program to address the needs of older adults with MCC and Type 2 Diabetes Mellitus. The final paper describes the results of a pragmatic randomized controlled trial of an online intervention for family caregivers of older persons with MCC and dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".