An exploration of sociological factors that contribute to physician burnout
Bibliographic record
Abstract
A discussion about mental health and how it relates to the medical profession is incomplete without exploring the concept of burnout. The implications of physician burnout are profound, and it is plaguing the medical community at epidemic rates. Current research focuses on which occupational factors may be contributing to this problem. Other approaches involve investigating the efficacy of building resilience at the individual level as a means of combatting burnout. Examining this issue through a broader lens and considering sociocultural factors that may be influencing how medicine is experienced by those in the field is remarkably untouched in the literature. This article will discuss how several changes in contemporary Canadian society may be underlying factors in physician burnout. The increasing penetrance of the internet into patient-physician interactions, the rise of online review platforms and widespread secularization in a domain that continues to face issues that evidence-based medicine fails to explain will all be addressed. This is merely a preliminary discussion to fortify current initiatives aimed at promoting awareness of and preventing physician burnout.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".