Personal values influencing career path in academic medicine
Bibliographic record
Abstract
To pursue research, education, and health policy in one's career, broadly defined as academic medicine, is one of the most important decisions of a trainee doctor's career. Despite this, there is scant literature on which factors influence trainees' choices towards clinical work or academic research. As the MD/PhD is a relatively young training path compared to the traditional PhD (Doctor of Philosophy) and MD (Doctor of Medicine) programs, it prompts the question: at the crossroads of a career, what sways the individual to select an MD, PhD, or MD/PhD program? This is a valuable question to be answered for trainees who are considering multiple career paths, for educators who want to guide undifferentiated students, and for policy makers who develop and coordinate research programs. "Intellectual stimulation" is the most consistently identified personal value which draws trainees to academic medicine. Mentorship is linked strongly to success in the field. Conversely, long training periods, a lack of autonomy, and financial considerations are deterrents from a career in academic medicine. Insight into the decision-making process is provided by recent trainees in these respective fields, as well as experienced academic clinicians.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".