Polypharmacy among persons residing in long-term care facilities before and during the COVID-19 pandemic in Canada: A retrospective cohort analysis
Bibliographic record
Abstract
,According to the World Health Organization, one in nine persons are designated older adults, or 60 years of age or older, and the percentage of the global population over 60 years will nearly double from 12% to 22% between 2015 and 2050 (World Health Organization [WHO, 2022]). Individuals living in long-term care facilities (LTCFs) are often frail, and present with co-morbidities that demand the administration of multiple drugs referred to as “polypharmacy”. Polypharmacy poses a significant concern for individuals in LTCFs due to several factors. The metabolic changes and reduced drug clearance associated with aging make older individuals more susceptible to adverse drug reactions and drug-drug interactions, resulting from the utilization of multiple medications (Dagli et al., 2014). Furthermore, the COVID-19 pandemic has brought attention to the significant vulnerability of individuals receiving care in LTCFs, attributable to both the potential for inappropriate polypharmacy and the direct risk of COVID-19 and its potential complications. There have been limited studies examining the prevalence and factors associated with polypharmacy among persons living in LTCFs, especially in the context of the COVID-19 pandemic in Canada. This study intends to fill this information gap by using secondary data to determine the prevalence and risk factors of polypharmacy among newly admitted Canadians in LTCFs before and during the COVID-19 pandemic. Identifying risk factors may lead to more effective and tailored therapy or even prevention of inappropriate prescribing especially in the context of the COVID-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".