The uptake of technologies designed to influence medication safety in Canadian hospitals
Bibliographic record
Abstract
BACKGROUND: There are many technologies designed to improve medication safety. Although limited evidence supports their use, there are pressures to implement them. OBJECTIVE: To determine the uptake of technologies designed to improve medication safety, plans for adopting technologies, attitudes towards technology use, and perceptions of medication error. Methods We performed a cross-sectional survey of pharmacy directors at Canada's 100 largest acute-care hospitals. RESULTS: Seventy-eight per cent of surveyed hospitals responded. Responding hospitals averaged 499 beds and 29% were teaching facilities. Hospital frequently used clinical pharmacy services (97% of hospitals), pharmacy-based intravenous admixture services (81%), computerized decision support modules for pharmacy order entry systems (77%), unit-dose drug distribution systems (75%) and computerized medication administration records (67%). Hospitals infrequently used bar-coding (9% of hospitals) and computerized physician order entry (9%). A majority of respondents and hospitals favoured expanded use of new technologies and planned for increased uptake. Respondents chose as their hospital's next investment: automated dispensing (33%), bar-coding (25%) and computerized physician order entry (12%). CONCLUSION: Canadian hospitals appear poised to make sizeable investments in poorly evaluated technologies that address medication safety.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".