Prioritizing Pharmaceutical Activities
Bibliographic record
Abstract
OBJECTIVES: The primary objective was to examine the consistency of prioritization decisions made by pharmacy residents in a simulated environment where the available resources are constrained. Secondary objectives were to rank the factors that influenced their prioritization and to compare the residents' results with those of Canadian pharmacy leaders. METHODS: We have developed a prioritization exercise that aims at evaluating how pharmaceutical activities are prioritized. The simulation was conducted with hospital pharmacy residents in 2 Quebec universities in 2011. RESULTS: Residents covered a similar number of activities in the prioritization simulation (mean 27 of 32). Teams tended to favor a broad range of services delivered less comprehensively. Participants ranked "perception of the favorable impact of the activity on health outcomes" higher than "conclusive evidence available to support the decisions." The relative weight attributed per domain was similar between pharmacy residents and pharmacy leaders, but their ranking of factors that influenced their decisions was different. CONCLUSIONS: Pharmacy residents opted to provide a wide range of services, but at a low level of comprehensiveness. The high variation between each team's coverage per activity in this simulation supports the observation that pharmacy residents do not agree on a core set of pharmaceutical activities that should be prioritized.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".