Current Status of Comprehensive Medication Management in Long-term Care Facilities: Focused on Australia, Canada, the United Kingdom and the United States
Bibliographic record
Abstract
Polypharmacy and potentially inappropriate medication use have increased among the residents of long-term care facilities (LTCF). Nevertheless, Comprehensive Medication Management (CMM) for LTCF residents was not implemented in Korea. The United Kingdom, Canada, the United States, and Australia have already introduced CMM for managing drug-related problems in LTCF. This study discussed the implications of developing the CMM service for LTCF residents in Korea by reviewing the system of these countries. The contents and requirements of CMM are investigated through relevant papers and official reports of the public institution of each country. The CMM service of these countries is regularly conducted based on their system and laws. In Canada, there are no additional requirements such as special education and qualifications for pharmacists providing CMM. But in other countries, it is preferred that pharmacists providing CMM are geriatric pharmacists or take the education that is equivalent to them. In addition, all these countries utilize national computer networks or electronic healthcare records that are used for executing CMM and for sharing with other medical experts, and then they are retained as official medical records. It is necessary to consider the system of other countries when introducing the CMM service for LTCF residents in Korea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".