Bibliographic record
Abstract
Until recently, much of the recent upsurge in interest in physician health has been motivated by concerns about improving patient care and patient safety and reducing medical errors. Increasingly, more attention has turned to examining how the management of mental illness among physicians might be improved within the medical profession and one key direction for change is the reduction of stigma associated with mental illness. I begin this article by presenting a brief overview of the stigma process from the general sociological literature. Next, I provide evidence that illustrates how the stigma of mental illness thrives in the medical profession as a result of the culture of medicine and medical training, perceptions of physicians and their colleagues, and expectations and responses of health care systems and organizations. Lastly, I discuss what needs to change by proposing ways of educating and raising awareness regarding mental illness among physicians, discussing approaches to assessing and identifying mental health concerns for physicians and by examining how safe and confidential support and treatment can be offered to physicians in need. I rely on strategically selected studies to effectively draw attention to and support the central themes of this article.
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 | 0.004 | 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 | 0.012 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".