A new care model reduces polypharmacy and potentially inappropriate medications in Long-Term Care
Bibliographic record
Abstract
Objectives: Assess the impact of a new pharmaceutical care model on (1) polypharmacy and (2) potentially inappropriate medication (PIM) use in long-term care facilities (LTCFs). \nDesign: Pragmatic quasi-experimental study with a control group. This multifaceted model enables \npharmacists and nurses to increase their professional autonomy by enforcing laws designed to expand \ntheir scope of practice. It also involves a strategic reorganization of care, interdisciplinary training, and \nsystematic medication reviews. \nSetting and Participants: Two LTCFs exposed to the model (409 residents) were compared to 2 control \nLTCFs (282 residents) in Quebec, Canada. All individuals were aged 65 years or older and residing in \nincluded LTCFs. \nMeasures: Polypharmacy ( 10 medications) and PIM (2015 Beers criteria) were analyzed throughout \n12 months between March 2017 and June 2018. Groups were compared before and after implementation \nusing repeated measures mixed Poisson or logistic regression models, adjusting for potential confounding variables. \nResults: Over 12 months, for regular medications, polypharmacy decreased from 42% to 20% (exposed \ngroup) and from 50% to 41% (control group) [difference in differences (DID): 13%, P < .001]. Mean number \nof PIMs also decreased from 0.79 to 0.56 (exposed group) and from 1.08 to 0.90 (control group) (DID: \n0.05, P ¼ .002). \nConclusions and Implications: Compared with usual care, this multifaceted model reduced the probability \nof receiving 10 medications and the mean number of PIMs. Greater professional autonomy, reorganization of care, training, and medication review can optimize pharmaceutical care. As the role of pharmacists is expanding in many countries, this model shows what could be achieved with increased \nprofessional autonomy of pharmacists and nurses in LTCFs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".