Bibliographic record
Abstract
OBJECTIVES: This study describes the literature of hospital pharmacy and identifies the journals most commonly cited by authors in the field, the publication types most frequently cited, the age of citations, and the indexing access to core journals. The study also looks at differing citation practices between journals with a wide audience compared to a national journal with a focus on regional issues and trends in the field. METHOD: Cited references from five discipline-specific source journals were collected and analyzed for publication type and age. Two sets were created for comparison. Bradford's Law of Scattering was applied to both sets to determine the most frequently cited journals. RESULTS: Three-quarters of all cited items were published within the last 10 years (71%), and journal articles were the most heavily cited publication type (n=65,760, 87%). Citation analysis revealed 26 journal titles in Zone 1, 177 journal titles in Zone 2, and the remaining were scattered across 3,886 titles. Analysis of a national journal revealed Zone 1 comprised 9 titles. Comparison of the 2 sets revealed that Zone 1 titles overlapped, with the exception of 2 titles that were geographically focused in the national title. CONCLUSION: Hospital pharmacy literature draws heavily from its own discipline-specific sources but equally from core general and specialty medical journals. Indexing of cited journals is complete in PubMed and EMBASE but lacking in International Pharmaceutical Abstracts. Gray literature is a significant information source in the field.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.106 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".