Prioritizing Pharmaceutical Activities: A Simulation Exercise
Bibliographic record
Abstract
New technologies, such as automated repackaging, robotic unit-dose cart-fill systems, and automated dispensing cabinets, have improved the efficiency, effectiveness, and quality of drug distribution systems. In addition, new pharmacy practice models have been introduced in which pharmacists accept responsibility and accountability for managing drug therapy (e.g., pharmaceutical care, medica tion therapy management). There is an abundance of evidence regarding the benefits of many pharmacy services for the quality and effectiveness of health care, 4-8 but the uptake of many evidence-based services has been slow and incomplete. As such, there is a relative paucity of literature about the decisionmaking processes that pharmacy managers and practitioners use to prioritize the pharmacy services that they provide. Given that available human and financial resources are limited, it is important for pharmacy managers and others in the profession to identify and understand the basis for their prioritization decisions. More specifically, they need to understand if the portfolio of services provided by a particular pharmacy depart ment is evidence-based, preference-based, or a result of random opportunities that have arisen in the hospital. A simulation exercise was developed to examine how hospital pharmacy managers make prioritization decisions. The primary objective of the exercise was to examine the con sistency of pharmacy managers’ prioritization decisions in a simulated environment with constraints on available resources. The secondary objective was to rank the factors influencing prioritization decisions and to compare individuals’ and teams’ rankings of these factors. METHODS
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".