Publication records and bibliometric indices of Canadian and U.S. pharmacy deans
Bibliographic record
Abstract
Background: As leaders and role-models in schools and colleges of pharmacy, Chief Executive Officer (CEO) deans must have a sufficient background and experience in research and scholarship. Objective: The primary purpose of this research was to characterise and compare the publication records and bibliometric indices of the current CEO deans at the schools and colleges of pharmacy (SCOP) in Canada and the United States (U.S). Methods: This was a cross-sectional study of pharmacy dean publication records and bibliometric indices using the Web of Science (WoS) database. Deans were identified using the Canadian website, Association of Faculties of Pharmacy. The methodology of Thompson and Nahata was used to conduct the WoS searches. The software programme developed by Soler was used to separate homologues and calculate bibliometric indices. Bibliometric indices generated included: lifetime publications, publications/year, h-index, m-quotient, lifetime citations, citations/year, and average citations/paper. The Kruskal-Wallis analysis of variance for nonparametric data was used to assess differences between groups. Results: Median bibliometric indices for Canadian pharmacy deans (N=10) vs. U.S. pharmacy deans (N=124) were as follows: No. of publications=57.5 vs. 20.5, Publications/year=3.5 vs. 0.5, h-index=14.5 vs. 8, Total citations =628.5 vs. 223.5, Citations/year=38.2 vs. 11.2. None of the differences were significant at p <0.05. Conclusion: Median bibliometric indices of Canadian pharmacy deans were higher but not significantly different from U.S. pharmacy deans.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.127 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".