[Not Available].
Bibliographic record
Abstract
CONTEXT: Few data are available on the use and consequences of decentralized automated dispensing cabinets (ADCs) in institutional settings. METHOD: This descriptive study examined compliance with published guidelines on the implementation of decentralized ADCs. The primary objective was to evaluate overall compliance, as well as compliance with specific steps in the medication cycle. The study was carried out at the Centre hospitalier universitaire (CHU) Sainte-Justine, a 500-bed mother-and-child hospital. The 2008 guidelines of the Institute for Safe Medication Practices (US) concerning the safe use of decentralized ADCs and the associated self-assessment tool (2009) were used to evaluate compliance at 30 days and at 120 days after implementation. RESULTS: From November 2009 to April 2010, 7 decentralized ADCs were brought into service at the CHU Sainte-Justine. Overall compliance with published guidelines increased from 66% to 74% between January and April 2010. For each process related to the safe use of the ADCs, the criteria were briefly described, along with the non-compliance components related to technological or organizational aspects of implementation. For each component for which practice was noncompliant with guidelines, the actions required to modify the equipment (i.e., technological aspects) were determined and conveyed to the manufacturer; similarly, modes of use requiring modification (i.e., organizational aspects) were determined and conveyed to the institution. CONCLUSION: This study has described the compliance of practices at the CHU Sainte-Justine with published guidelines of the Institute for Safe Medication Practices. The use of published guidelines can help to guide both the technological and organizational aspects of implementing decentralized ADCs. [Publisher's translation].
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.664 | 0.414 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".