The impact of online medication reviews and educational workshops on deprescribing during the COVID-19 pandemic: a controlled before-after study
Bibliographic record
Abstract
Abstract Objectives The South Peace Polypharmacy Reduction Project is a quality improvement project in three communities in rural Canada that aimed to reduce polypharmacy and inappropriate prescribing practices in older adults. This study aims to evaluate the impact of a multifaceted intervention consisting of online team-based medication reviews and educational workshops on the number of chronic medications. Methods A controlled before-after design was used to compare if a decrease in the number of chronic medications was associated with the intervention comprising of online team-based medication reviews and educational workshops, compared with two matched control groups that received either a standard medication review or no medication review. Logistic regression models fit with generalized estimated equations were used to identify the impact of the interventions on decreasing the number of chronic medications. Key findings Following a medication review, the percentage of individuals that had deprescribed at least one medication was highest in the intervention group (52%), followed by the medication review controls at 45%, and 36% in non-medication review controls. Individuals in the intervention group were 20% more likely to have at least one medication deprescribed than individuals in the medication review control group (adjusted odds ratio: 1.20; 95% CI: 1.03 to 1.39), whereas they were 42% more likely to deprescribe at least one medication compared with non-medication review controls (adjusted odds ratio: 1.42; 95% CI: 1.25 to 1.61). Conclusions Online team-based medication reviews had a significant impact on decreasing the number of chronic medications in older adults. Furthermore, providing healthcare providers with education can complement the role of other healthcare interventions.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".