Research citation analysis of nursing academics in Canada: identifying success indicators
Bibliographic record
Abstract
AIM: This article is a report of a citation analysis of research publications by Canadian nursing academics. BACKGROUND: Citation analysis can yield objective criteria for assessing the value of published research and is becoming increasingly popular as an academic evaluation tool in universities around the world. Citation analysis is useful for examining the research performance of academic researchers and identifying leaders among them. METHODS: The journal publication records of 737 nursing academics at 33 Canadian universities and schools of nursing were subject to citation analysis using the Scopus database. Three primary types of analysis were performed for each individual: number of citations for each journal publication, summative citation count of all published papers and the Scopus h-index. Preliminary citation analysis was conducted from June to July 2009, with the final analysis performed on 2 October 2009 following e-mail verification of publication lists. RESULTS: The top 20 nursing academics for each of five citation categories are presented: the number of career citations for all publications, number of career citations for first-authored publications, most highly cited first-authored publications, the Scopus h-index for all publications and the Scopus h-index for first-authored publications. CONCLUSION: Citation analysis metrics are useful for evaluating the research performance of academic researchers in nursing. Institutions are encouraged to protect the research time of successful and promising nursing academics, and to dedicate funds to enhance the research programmes of underperforming academic nursing groups.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | MetaresearchBibliometricsScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Other design | low |
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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.107 | 0.164 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".