Bibliographic record
Abstract
1Some of the primary objectives of the PPMI for hospitals and health systems are to describe optimal pharmacy practice models that support the provision of safe, effective, efficient, and accountable medication-related care for patients; to enhance the capacity of pharmacy technicians; to identify core patient-care-related services that should be consistently provided by departments of pharmacy; to foster understanding of and support for optimal pharmacy practice models by patients and caregivers, health care professionals, health care executives, and payers; to identify existing and future technolo gies required to support optimal pharmacy practice models; and to identify specific actions that hospital pharmacists should take to implement optimal practice models. In November 2010, ASHP held an invitational conference, the Pharmacy Practice Model Summit, at which over 100 pharmacy leaders met to reach consensus on the types of pharmacy practice models that would best serve the needs of both patients and the hospitals and health systems that deliver care to those patients. The consensus recommendations from the summit are numerous, but the following are some of the more noteworthy 2
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".