Bibliographic record
Abstract
In the last decade, there has been a discrepancy between the increasing recognition for research involvement in medical training and the stagnation in the number physician-scientists. Health research funding cutbacks, inadequate mentorship, heavy schedules, and unfamiliarity with scientific methodology are obstacles that limit research interest amongst junior medical learners and cause attrition of promising physician-scientist in training. This article outlines five strategies to promote and facilitate the development of physician-scientists with the understanding that research is integral to clinical excellence. Some of the ways the undergraduate and postgraduate medical curricula can better lend themselves to producing clinicians with the skillset to address clinical uncertainties through an evidence-based approach are: partnerships between healthcare and academia, increasing admission to MD/PhD and Clinical Investigator programs, establishing fundamentals of scientific thinking, long-term research mentorship, facilitating knowledge translation.
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 | 0.113 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".