Publishing results of medical research: the effect of pressure or a natural intention to advance scientific knowledge?
Bibliographic record
Abstract
Introduction We expect that the issue discussed in this article will contribute to this academic debate and reveal the question of publishing scientific research results in one of the extremely important social areas of knowledge, namely medicine. In our opinion, it will provide arguments to answer the question concerning the ways in which institutional and environmental pressure influenced the publication activity of scientists in this scientific discipline at Polish medical universities. Aim of the research The aim of this article is to present the results of a survey of the intensity of publication activity among medical science researchers at Polish universities. This intention was accomplished using bibliometric analysis based on quantitative indices depicting the publication activity of medical university employees in Poland who located the results of their research efforts in the field of “medicine”, which were published between 2017 and 2023. Additionally, we wish to unveil the main topic clusters. Material and methods The bibliometric study included publications that were published between 2017 and 2023 by employees of public medical universities in Poland, and at the same time these entities are included in the World University Rankings 2024 in the field of clinical and health for the region of Poland, which is commonly used in such studies. Results and conclusions The results of our research in this area completed a subset of the broad scientific landscape associated with the ‘publishing game’. In medicine, as in other socially high-status disciplines, competition for prestige is the norm and publishing is a way to gain recognition in the community.
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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 | 0.125 | 0.535 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".