Factors Affecting the Use of Medical Articles for Citation and Academic Reference
Bibliographic record
Abstract
Introduction: Increases in publication quantity and the onset of open access have increased the complexity of conducting a literature search. Bibliometric markers, like impact factor (IF), have traditionally been used to help identify high-quality research. These markers exist amongst a variety of other factors, which poses the following question: what factors are examined when considering articles for clinical and academic research? Objective: To determine what factors are involved when authors choose citations to include in their publications. Methods: A voluntary and anonymous questionnaire-based survey was distributed to medical students, residents, and faculty from multiple medical schools across Canada during the 2020/2021 academic year. Survey ratings were scored on a 5-point Likert scale and open word response. Results: The study collected 156 complete sets of responses including 78 trainees (61 medical students and 17 residents), and 78 faculty. Language of the article (3.93) and availability on PubMed/Medline (3.77) were found more important than country of origin (2.14), institution (2.26), and IF (2.97). Trainees found the following factors more important than faculty: year of publication (3.94 vs 3.47, p = 0.0016), availability on Google/Google Scholar (2.51 vs 1.88, p = 0.0013), Open-access (2.46 vs 1.87, p = 0.0011), and Free access (2.73 vs 2.31, p = 0.049). Conclusion: Our study identified differences in faculty and trainee literature search preferences, bias towards English language publications, and the movement towards online literature sources. This knowledge provides insight into what biases individuals may be exposed to based on their language and literature search preferences. Future areas of research include how trainees' opinions change over time, identifying trainee ability to recognize predatory journals, and the need for better online journal article translators to mitigate the language bias. We believe this will lead to higher quality evidence and optimal patient care amongst healthcare workers.
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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 | 0.030 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".