A Qualitative Investigation on Chronic Disease Management and Prevention Among Older Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: To qualitatively describe experiences of chronic disease management and prevention in older adults (age ≥65 years) during COVID-19. APPROACH: Qualitative descriptive approach. SETTING: Data collected online via telephone and video-conferencing technologies to participants located in various cities in British Columbia, Canada. Data analyzed by researchers in the cities of Vancouver and Kelowna in British Columbia. PARTICIPANTS: Twenty-four community-living older adults (n = 24) age ≥65 years. METHODS: Each participant was invited to complete a 30-to-45-minute virtual, semi-structured, one-on-one interview with a trained interviewer. Interview questions focused on experiences managing health prior to COVID-19 and transitioning experiences of practicing health management and prevention strategies during COVID-19. Audio recordings of interviews were transcribed verbatim and analyzed thematically. RESULTS: The sample's mean age was 73.4 years (58% female) with 75% reporting two or more chronic conditions (12.5% none, 12.5% one). Three themes described participants' strategies for chronic disease management and prevention: (1) having a purpose to optimize health (i.e., managing health challenges and maintaining independence); (2) internal self-control strategies (i.e., self-accountability and adaptability); and (3) external support strategies (i.e., informational support, motivational support, and emotional support). CONCLUSION: Helping older adults identify purposes for their own health management, developing internal control strategies, and optimizing social support opportunities may be important person-centred strategies for chronic disease management and prevention during unprecedented times like COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".