Use of prescribed and non-prescribed medicines by the elderly: implications for who chooses, who pays and who monitors the risks of medicines
Bibliographic record
Abstract
Abstract Objective To examine overall medicine use and the prevalence and distribution of use of the different types of medicines by the community dwelling elderly; and to discuss the implications of these use profiles for the health of this population. Method We examined aggregate levels of self-reported use of prescription (Rx) and over-the-counter (OTC) medicines, and natural health products (NHPs) among community-dwelling elderly. Analysis focused on the relative balance of use of different types and combinations of medicines, and differences between five-year age categories, and sex. Setting Data are based on Canada's National Population Health Survey (1996/1997), and reflect population estimates of medicines use (over the previous two days) by elderly persons living in Ontario, Canada. Key findings In the total population, and in age- and sex-groups, a quarter of respondents reported using no medicines; use of OTC medicines (56%) was more prevalent than use of prescription medicines (48%). Seven per cent of respondents reported using NHPs. The proportions of elderly people using combinations of different types of medicines are reported. Conclusion The study findings place the use of prescription medicines by elderly people in the context of overall use of medicines. Over half of the study respondents were using one or more OTC medicines. There is a need for further examination of how individuals select and gain access to medicines of different types (distinguishing medicines selected for the patient and those selected by the patient), who pays for those medicines (distinguishing insured products and those purchased out-of-pocket), and what role pharmacists and other healthcare professionals ought to play in mediating the potential risks arising from medicine use in the elderly population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".