Chapter D ‐ Drug Distribution Systems 2007/08 Hospital Pharmacy in Canada Report www.lillyhospitalsurvey.ca Page 25 D DRUG DISTRIBUTION SYSTEMS
Bibliographic record
Abstract
Drug distribution systems in the hospital setting should ideally prevent medication errors from occurring. When errors do occur, the system should facilitate their early detection, enabling corrective steps to be taken to prevent their recurrence and to minimize any adverse effects on the patient. Hospital drug distribution systems should also facilitate the appropriate allocation and use of available resources. The unit‐dose drug distribution system is endorsed by The Canadian Society of Hospital Pharmacists as the drug distribution system of choice in organized healthcare settings because it provides improvements in medication safety, overall system efficiency, job satisfaction, and effective use of human resources.1 • Centralized unit dose systems, in which unit dose medications are selected and assembled in the pharmacy department, for each patient, were reported to be in use by 64 % (103/162) of all respondents (Table D‐1). For hospitals with 201‐500 beds and for hospitals with more than 500 beds, there was little change in the per cent of respondents reporting use of centralized unit dose systems in 2007/08, compared to the results of the 2005/06 survey. The per cent of respondents reporting the use of centralized unit dose systems in hospitals with 100‐200 beds was 48 % (13/27) in 2005/06 compared to 36 % (12/33) of hospitals with 50‐200 beds in 2007/08. The inclusion of smaller hospitals in the 2007/08 survey may have contributed to this change. • Regional differences in the use of centralized unit dose systems were noted with 48 % (10/21) of
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.212 | 0.072 |
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".