Factors related to the frequency of citation of epidemiologic publications
Bibliographic record
Abstract
BACKGROUND: Previous studies have demonstrated that the frequency with which a publication is cited varies greatly. Our objective was to determine whether author, country, journal, or topic were associated with the number of times an epidemiological publication is cited. METHODS: We used outcome-based sampling and investigated one public health issue - child injury prevention, and one clinical topic - coronary artery disease (CAD) prevention. Using the Institute for Scientific Information's (ISI) Web of Science(R) databases, we limited searches to full articles involving humans published in English between 1998 and 2004. We calculated the citation rate and, after frequency-matching on year of publication, selected the 36 most frequently cited and 36 least frequently cited articles per year, for a total of 252 highly-cited and 252 infrequently-cited articles per topic area (child injury prevention and CAD prevention). RESULTS: Highly-cited articles in both CAD and child injury prevention were more likely to be published in medium or high impact journals or in journals with medium or high circulations. They were also more likely to be published by authors from U.S. institutions. Among articles examining CAD prevention, the highly-cited articles often involved risk factors, and the association between topics and frequency of citation persisted after adjusting for impact factor. Among articles addressing child injury prevention, topic was not statistically associated with citation. CONCLUSION: Journal and country appear to be the factors most strongly associated with frequency of citation. In particular, highly-cited articles are predominantly published in high-impact, high-circulation journals. The factors, however, differ somewhat depending on the area of research the journals represent. Among CAD prevention articles, for example, topic is also an important predictor of citation whereas the same is not true for articles addressing injury prevention. CONDENSED ABSTRACT: Our objective was to determine whether author, country, journal, or topic were associated with the number of times an epidemiological publication is cited. We used outcome-based sampling and investigated one public health issue, child injury prevention, and one clinical topic, coronary artery disease (CAD) prevention. Using the Institute for Scientific Information (ISI) Web of Science(R) databases, we limited searches to full articles involving humans published in English between 1998 and 2004. We calculated the citation rate and, after frequency-matching on year of publication, selected the 36 most frequently cited and 36 least frequently cited articles per year, for a total of 252 highly-cited and 252 infrequently-cited articles per topic area (child injury prevention and CAD prevention). Highly-cited articles in both CAD and child injury prevention were more likely to be published in medium or high impact journals or in journals with medium or high circulations. They were also more likely to be published by authors from U.S. institutions. Among articles examining CAD prevention, the highly-cited articles often involved risk factors, and the association between topics and frequency of citation persisted after adjusting for impact factor. Among articles addressing child injury prevention, topic was not statistically associated with citation.
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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 | 0.013 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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, 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".