To maximise impact, hospital pharmacists need to increase visibility
Bibliographic record
Abstract
Hospital pharmacists are essential to patient care and the integrity of the healthcare system. By applying their expertise as medication experts, they act to improve patient outcomes and reduce the cost of medication therapy. These outcomes have been demonstrated by numerous studies. In one meta-analysis, it was observed that the addition of a hospital pharmacist on interdisciplinary rounds in the intensive care unit (ICU) reduced adverse drug events, patient mortality, and length of stay.1 Another study found that the introduction of a clinical pharmacist to the ICU team led to cost savings of $1977 (€1822) on medication over the 24-week study.2 Despite their positive impact on patients and the healthcare system, hospital pharmacists are underrepresented in the media and with the public. These gaps in representation contribute to a lack of visibility within and outside of the hospital setting. Visibility is important as it is linked to professional advocacy. The lack of visibility may result in underrepresentation of hospital pharmacists in leadership or governance activities. One study evaluating healthcare professional representation on hospital boards in New York City found that while physicians and nurses were represented, not a single pharmacist was found on hospital boards in the city.3 Encouragingly, one pharmacist was found to sit on the governing body of a federally qualified health centre.3 One way to increase visibility is by increasing public knowledge of the … Correspondence to Dr Peter Chengming Zhang, University of Toronto Leslie Dan Faculty of Pharmacy, Toronto, ON, Canada; petercm.zhang{at}mail.utoronto.ca
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 | 0.028 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.018 |
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".