Canadian Medical Education Journal Editorial
Bibliographic record
Abstract
hits. Judging by my own experience and the few (compared to 1.5M) articles I have read, we don’t find a very happy family portrait. Many of the articles which compose this issue of CMEJ speak to high levels of stress and burnout, worries about finding residency positions and jobs, and the experience of various forms of harassment. Medical education can sometimes be harsh rather than happy. Sorry; we are only the messengers here. Rather than drawing weapons and flailing them at the screen in front of you, let’s deal with this situation as best we can. I believe the first step is acknowledging the nature and magnitude of the problem, then recommending and implementing usually short term treatments, and finally – but perhaps most importantly – moving upstream1 to address the sources of stress. The literature is replete with studies that identify the rather serious consequences of stress: depression,2 decreased job satisfaction and disillusionment with the medical profession,2,3 psychological distress,2,4 absenteeism and disability,2 exhaustion and decreased motivation.3 This is clearly an important issue. To address these problems, some schools have wellness programs of various kinds. There are extracurricular stress reduction programs actually implemented2,5,6 or recommended.7 I am quite sure the vast majority of Canadian and US medical schools have and make readily available various forms of support and counselling to their trainees as strongly suggested by Benbassat et al.7 These measures are a good start. It seems few places have successfully addressed the sources of some of these stressors. As reported by
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.223 | 0.078 |
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".