Bibliographic record
Abstract
When I first started as a medical student on the wards, I remember very strongly the words of the professor of medicine at the time, who seemed bored with clinical medicine. I can remember very clearly wondering how one could reach this cynical and worldweary state of mind and aimed from that formative stage of my medical career to avoid this end. I look back now, some 25 years down the road, and realise that I too have developed the same attitudes and wonder whether this is just me or whether there is inevitability about our medical life journey that leads us to this particular state of grace. This struck home for me recently where, in my home city of Melbourne, there have been two junior registrars who have committed suicide during the past 12 months. This highlights the fact that medical practitioners and others in the healthcare industry have an increased risk of death from suicide. A recent literature review found that male medical practitioners may be twice as likely to commit suicide as other professional males in general, and female medical practitioners are four to six times more likely to commit suicide than other professional women.1 Obviously this is not only a tragedy for all the families and work colleagues involved, but also an incredible waste of talent and of the community’s investment in the individual’s healthcare training. Unhappy doctors are a worldwide phenomenon.2,3 The Canadian Medical Association,4 in its statement on physician health and wellbeing, stated: “There are indications that physician stress is on the rise; increasingly, medical students, residents and practising physicians are voicing distress and seeking assistance in coping with stresses in their training, practice and personal lives”. There is no reason to suspect that sport and exercise medicine (SEM) physicians …
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.030 |
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".