COMMUNITY MANAGEMENT OF CHRONIC DISEASE: AN ALTERNATIVE MODEL FOR SENIORS WITH MULTIPLE MORBIDITIES
Bibliographic record
Abstract
Managing the increasing prevalence of chronic diseases (CD) has become a priority globally. However, the feasibility of using a CD self-management approach with older individuals with multiple morbidities, and reduced physical and cognitive abilities to deal with the impact of these conditions in their day-to-day lives, is increasingly being challenged. We propose CD management for older individuals is best done in a collective community context using a community capacity building approach that actively engages residents of the community as a whole in raising awareness, identifying neighbours at risk and providing peer-led education and ongoing mentoring to improve persistence with positive lifestyle changes. Employing this approach has shown a significant increase in screening for osteoporosis in a naturally occurring retirement community (NORC) of seniors in London, Ontario Canada (n=105; mean age=80.5 ±6.9; p<.001). We are now trialing this in a further study of osteoarthritis prevention and management. Critical elements of our model include training community residents to become CD advocates and coaches, building relationships and sustained supportive networks and peer mentorship programs, and designing tools to make it easier for older community members to address key issues with their family physicians. Our findings suggest guidance is more important than knowledge to enable behavior change. Leveraging knowledge from physician to community advocates to peers/patients at a community level brings economies of scale. This process is an essential link along the continuum of RCT, to guideline development, to screening, assessment and management of chronic diseases and offers a new evidence-based approach.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".