Bibliographic record
Abstract
even work on nursing units to directly address production and dosepreparation issues related to medications. Pharmacists will no longer be required to enter orders at a location distant from patients, and they will have limited need to spend hours in the library researching a particular case so that they can assist in designing the best possible medication regimen for the patient. All the knowledge and information that pharmacists need will be at their disposal, and, conversely, the distribution system will function without input from pharamcists. Pharmacists will be truly free to work on the nursing units and to take full advantage of the opportunities associated with pharmaceutical care, achieving both the objectives of CSHP 2015 and the rewarding careers that they envisioned when they first graduated as pharmacists. The ultimate outcomes of such a bold step would be significant and measurable improvements in quality of care and the safety of medication use. There is no better reason to grasp the opportunity offered by these technologies than the prospect of truly disintermediating pharmacists from their manual, product-based environment and fulfilling the promise made to patients through the Oath of Maimonides.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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".