Thorough Planning and Full Participation by Pharmacists Is Key to MOE/MAR Success
Bibliographic record
Abstract
Pharmacy's Role in MOE/MAR Development Developing UHN's technical solution to support MOE/MAR proved to be a highly complex undertaking.Many stakeholder groups were involved in integrating the Pharmacy departmental application with the existing hospital information system (HIS) that was to be used by physicians, nurses and pharmacists for MOE/MAR.These included the Pharmacy department, the organization's information technology department (known as Shared Information Management Services (SIMS)), the Nursing Informatics Department and clinicians (e.g., physicians, nurses, pharmacists).In developing the overall MOE/MAR strategy, the Pharmacy department's interests in keeping its own application were well understood by the senior leadership at UHN.The initial planning that led to UHN's decision to remain with the existing vendors and develop interfaces between the HIS and Pharmacy departmental application was led by SIMS, with input from the Pharmacy department, based on the costs, feasibility and ability to deliver the project in a timely fashion.Although this decision did ultimately receive the support of the Pharmacy department,
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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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".