Pharmacy-led Medication Management Services in Long-term Care Facilities: Lessons from other Countries for Korea
Bibliographic record
Abstract
The elderly usually have a high risk of drug-related problems by polypharmacy, therefore they are in need of drug management in long-term care facilities. This study aims to obtain the implications for developing a drug management system in long-term care facilities by pharmacists suitable for Korea by reviewing the drug management programs in long-term care facilities in countries that experienced population aging first. The United States, Canada, Australia, and Japan have enacted laws to optimize drug management in long-term care facilities according to the social demands of the aging population and operate specific programs based on those laws. Drug management programs in longterm care facilities operate in a variety of forms to suit the circumstances of each country. In long-term care facilities, pharmacists participate in the medication regimen review, in setting up drug related service frames and in developing relevant policies of the facilities. The results of the pharmacist s medication regimen review are not only provided to the doctor but also included in the medical record and kept for a while. Pharmacists emphasize cooperation with physicians and other health practitioners for proper drug management in long-term care facilities. In Korea, where the number of long-term care facilities is increasing along with the surge in the elderly population, it is necessary to develop a drug management system by pharmacists for safe drug use in long-term care facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".