Optimizing the Use of Automated Dispensing Cabinets
Bibliographic record
Abstract
INTRODUCTION According to the Hospital Pharmacy in Canada annual report for 2005/2006,1 32% of respondents to a survey of Canadian hospitals with at least 100 beds reported use of automated dispensing cabinets, an increase from only 20% in a similar survey conducted 2 years earlier.2 The automated dispensing cabinet is one of many technologies that may be implemented as hospitals and other health care organizations work toward computerization and automation of medication-use systems. These units can be simple to operate, but a full understanding of the potential risks associated with their use is crucial to avoiding unforeseen sources of error. Implementation and management of automated dispensing cabinets requires an interdisciplinary approach, and pharmacists’ contributions to the team are key. Those responsibilities include implementing, as part of the pharmaceutical care process, various practices to ensure safe outcomes, as well as medication distribution and related operational procedures. Guidance on the appropriate use of automated dispensing cabinets is available through a variety of sources, including the Institute for Safe Medication Practices (ISMP) in the United States3 and the American Society of Health-System Pharmacists.4 The current article, much of which is excerpted, with permission, from a recent issue of the ISMP Canada Safety Bulletin,5 highlights commonly encountered challenges and provides strategies for optimizing the use of automated dispensing cabinets.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".