Profile of Centralization Practices for Preparation of Non-Hazardous Drugs in Quebec Hospitals
Bibliographic record
Abstract
Abstract Background The preparation of many drugs intended for parenteral administration is centralized in the pharmacy of healthcare institutions. However, no data are available describing the range of drugs with centralized preparation. The objective was to establish a profile of centralization practices for the preparation of non-hazardous drug doses in the pharmacy departments of Quebec healthcare institutions. Methods For this cross-sectional descriptive study, an e-mail survey was distributed in March 2017 to the directors of the pharmacy departments of Quebec healthcare institutions. Respondents were asked to estimate the percentage of parenteral drug doses that were prepared centrally in the pharmacy, the name of each drug prepared this way, the criteria used to select drugs for central preparation, and the barriers to centralizing preparation of drug doses. Only descriptive statistical analyses were performed. Results Of the 30 directors of pharmacy departments invited to participate, 27 (90 %) responded, representing a total of 40 Quebec healthcare facilities. Overall, 232 individual drugs were centrally prepared in one or more of these facilities, for an overall median of 22 drugs per facility (min: 1, max: 101). Conclusions This is the first survey in Quebec and indeed all of Canada to identify the many medications that are centrally prepared in hospital pharmacies. The survey showed that the selection of drugs for central preparation differed widely across facilities. It would be desirable for pharmacy departments in this province to collaborate on standardizing practices for central preparations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".