Bibliographic record
Abstract
To the Editor: While burnout in medical students, residents, and early-career physicians has been recently investigated,1,2 a significant and distinct subgroup of trainees has been overlooked in clinician burnout research: MD/PhD trainees. There are important factors that differentiate this group from other clinical trainees, and as such, existing research may not reflect burnout rates amongst MD/PhD trainees. Specifically, there are potential risk factors and protective factors that merit being considered for future research. MD/PhD trainees have longer training durations than their MD-only counterparts, which has been estimated to amount to an average financial loss of $2.3 million (Canadian dollars) per trainee.3 This estimate was based on debt and potential years of earnings lost, but since MD/PhD trainees are generally older, there may be additional financial pressures that go along with life changes such as getting married, purchasing a home, or starting a family. Many MD/PhD trainees looking to establish a career in academia may also feel the added stress of preparing for two careers in a limited time frame. Unfortunately, the existing curriculum in medical school and residency offers limited time for research endeavors, making academic-track positions difficult to attain. On the other hand, MD/PhD training may protect against burnout from stress associated with residency matching, as MD/PhD trainees may have had more time to determine their desired medical specialty, and may have had opportunities to interact and form long-term relationships with clinicians in their desired specialty. In addition, they may have publication records and other accolades that enhance their odds of matching to their desired specialty. It is unclear what role social support may play in burnout among MD/PhD trainees. It is possible that MD/PhD trainees spend their free time on research and have less time to seek social support from friends, family, or resources at school, or it may be that they have had more time to develop social support networks over the course of their training. MD/PhD trainees may also be more likely to be married, which has been previously shown to be a protective factor against burnout.1 Ultimately, the lack of data on MD/PhD trainees leaves us to wonder about the net effect on burnout of these potential risk factors and protective factors. Future research should seek to investigate these questions and determine the nature of the stressors that MD/PhD trainees experience. This will inform the implementation of strategies within MD/PhD training programs to mitigate potential risks. Isabelle A. VallerandMD/PhD student, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected]
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".