Publication records and bibliometric indices of Canadian and U.S. pharmacy deans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it