Minor Ailments in Older Adults: A Study of Prevalence and Health-Related Behaviors
Bibliographic record
Abstract
BACKGROUND: With chronic diseases such as diabetes and hypertension garnering much needed attention in health care, minor ailments can go somewhat unnoticed. OBJECTIVE: To quantify prevalence, symptom burden, and health-related behaviors relative to 31 minor ailments in older adults. METHODS: A cross-sectional survey was carried out in 1 Canadian province. Participants were asked about symptoms experienced over a 2-week period, the disruption to daily activities they incurred, and treatment actions undertaken. RESULTS: Three-hundred and fifty-six adults with an average age of 72.8 years completed a questionnaire. Participants reported an average of 6.2 ailments over the time period. Back pain (46.9%), joint pain (44.4%), and insomnia (38.5%) were most common. The vast majority had been an issue for longer than 2 weeks. OTC medicines were the most common treatment choice in 16 situations, while doing nothing/watchful waiting was the main recourse in 13. OTC medication was common for headache (63.2%) and heartburn (61.2%), while doing nothing/watchful waiting was common for tinnitus (80.5%) and loneliness (74.2%). Satisfaction and confidence in self-management were highest for acute conditions like headache and cold sores. CONCLUSION: Minor ailments are common and diverse in older adults, with many chronic in nature, and a tendency for proactive self-care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".