Bibliographic characteristics of the research output of pediatric anesthesiologists in Canada Caracteristiques bibliographiques de la production d'etudes de recherche des anesthesiologistes pediatriques au Canada
Bibliographic record
Abstract
Purpose Various bibliometric citation indices have been used to evaluate research productivity and scientific impact, but recently, Hirsch’s h-index has gained widespread recognition. Although described initially for physical sciences, h-indices are being used to assess research productivity and impact in other disciplines. Methods In this descriptive study, Scopus TM and Web of Science citation databases were used to identify the bibliographic characteristics of pediatric anesthesiologists from all university affiliated departments of pediatric anesthesia in Canada up to May 2009. For each anesthesiologist, the h-index, mean citations per publication, total number of publications, total number of citations, and year of first publication were determined. Results A study population of 151 pediatric anesthesiologists was identified. The range of h-index values for this cohort was 0-32 with a median (interquartile range) of 2 (1-5). The 90 th percentile was 8.0. The median (interquartile range) number of citations per publication was 6 (1-15), with a range of 0-87. The median (interquartile range) number of publications was 4 (1-9) with a range of 0-165. Conclusions We describe the bibliographic characteristics of the research output of pediatric anesthesiologists in Canada. This study highlights the growing influence of scientometrics on the evaluation of scientific performance in medical specialties.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.084 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".