Bibliographic record
Abstract
Evidence-informed interventions to address the factors affecting resident wellness are essential. We were very interested to read the interventions Drs. Wang and Myers described in response to our research. Their suggestions of a “significant other day,” “resident parent support groups,” “a ‘big brother/big sister’ program,” and “e-mails from chief residents” to highlight personal nonacademic events have the potential to help enhance and leverage residents’ personal support network (i.e., partners, friends). Exploring the implementation and outcomes of these (as well as other) interventions to examine their feasibility and effectiveness will be a critical next step in understanding how to mitigate the negative effects of training on residents’ relationships. We would like to emphasize that in addition to individually focused interventions for residents like the ones proposed by Drs. Wang and Myers, addressing systemic issues arising from the training environment is equally important. Tackling problematic medical culture and curriculum structures (formal, informal, hidden) that reinforce the poor work–life balance that residents experience is overlooked and essential for addressing the negative impact the training environment can have on resident relationships. We hope our report will continue to foster dialogue that generates interventions—at both the individual and curriculum level—to improve resident wellness. Marcus Law, MD, MBA, MEdAssociate professor of family medicine, director, Foundations, MD Program, University of Toronto, and director of medical education, Michael Garron Hospital and Toronto East Health Network, Toronto, Ontario, Canada; ORCID: http://orcid.org/0000-0001-9746-6381; [email protected]; Twitter: @LawMarcus. Maria Mylopoulos, PhDAssociate professor, Faculty of Medicine, and scientist, Wilson Centre, University of Toronto, Toronto, Ontario, Canada; ORCID: http://orcid.org/0000-0003-0012-5375. Paula Veinot, MHScIndependent research consultant, Toronto, Ontario, Canada.
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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 | 0.007 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.032 | 0.054 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".