Understanding chronic disease management in older adults during the COVID-19 pandemic
Bibliographic record
Abstract
Background Chronic diseases are prevalent in Canada’s aging population, creating importance for older adults (age ≥65 years) to practice positive health behaviours (e.g., physical activity, healthy diet) for chronic disease management. However, novel coronavirus (COVID-19) prevention strategies of quarantining, social isolation, and physical distancing may compromise one's ability to manage health and thus, increase risk of adverse health events. Purpose To develop an understanding of chronic disease management in community-living older adults (age ≥65 years) during the COVID-19 pandemic. This included quantitatively evaluating a student-led Community Outreach teleheAlth program for Covid education and Health promotion (COACH) (Chapter 2), and qualitatively exploring the management strategies of older adults during COVID-19 and COACH participation (Chapter 3). Methods Chapter 2: In a single-group, pre-post study (n = 75), multiple paired sample t-tests were used to examine COACH’s effects on: (1) health directed behaviour (primary outcome); (2) perceived depression, anxiety, and stress; (3) social support; (4) health-related quality of life; (5) health promotion self-efficacy; and (6) self-management indicators. Chapter 3: A subset of COACH participants (n = 24) participated in semi-structured interviews. Interpretive description was the guiding methodological framework, and thematic analysis was performed to categorize the data. Results Chapter 2: Participants’ mean age was 72.4 years (59% female), with 80% reporting two or more chronic conditions. There were significant improvements in health directed behaviour (p < .001, d = 0.45). After applying Bonferroni correction on secondary outcomes, results showed significant improvement in self-efficacy (p <.001, d = 0.44) and significant decrease in mental health-related quality of life (p < .001, d = -1.69). Chapter 3: Participants’ mean age was 73.4 years (58% female) with 75% reporting two or more chronic conditions. Participants described purposes for optimizing their health, maintaining a sense of control, and using social support to optimize their management efforts. COACH further supported participants during COVID-19 through coach interactions and knowledge and skill development. Conclusion Chronic disease management in older adults can be described with identifying purposes to optimize health, followed by using internal and external motivators (like COACH) to support their self-management efforts during COVID-19.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".