Implementation of a shared research database to increase medical student awareness and involvement in urology research
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess the effect of a shared institutional research database on medical students' scholarly work, perceived research competency, and self-reported satisfaction. METHODS: An institutional inventory database was created on Google Sheets with a listing of available mentors and a description of their ongoing research projects. The inventory database was shared with interested students and faculty. Students who agreed to participate were surveyed pre- and post-inventory. Survey questions assessed student demographics, prior research experience, and their perception of research competency and satisfaction. The number of presentations, publications, and articles pre- and post-inventory were also abstracted. Survey responses were compared using the Mann-Whitney U test. RESULTS: A total of 20 students were surveyed pre-inventory and at a median followup of six months (5-7) post-inventory. There was a significant increase in scholarly presentations and publications post-inventory (p<0.05 for all). Furthermore, post-inventory, students reported feeling more confident in establishing an academic career, finding good mentors, managing their relationship with their mentor, managing professional challenges, and effectively showcasing themselves professionally and describing their research (p<0.05 for all). More than 65% of students agreed or strongly agreed that the database was easy to use, accessible, transparent, and would like a similar database created for other specialty departments. CONCLUSIONS: After performing mentorship-guided research through an institutional research database, medical students felt more confident in their ability to perform research and produced more scholarly work. Therefore, we recommend a research database be created across all institutional departments to foster interest in conducting research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".