Severe acute respiratory syndrome and its impact on professionalism: qualitative study of physicians' behaviour during an emerging healthcare crisis
Bibliographic record
Abstract
OBJECTIVE: To explore issues of medical professionalism in the context of severe acute respiratory syndrome (SARS), a new emerging health threat. DESIGN: Qualitative interviews analysed with grounded theory methodology. SETTING: University hospitals in Toronto, Canada, during the SARS outbreak in 2003. PARTICIPANTS: 14 staff physicians from divisions of infectious diseases, general internal medicine, and critical care medicine. RESULTS: Of 14 attending physicians, four became ill during the outbreak. Participants described their experiences during the outbreak and highlighted several themes about values inherent to medical professionalism that arose during this crisis including the balance between care of patients and accepted personal risk, confidentiality, appropriate interactions between physicians and patients, ethical research conduct, and role modelling of professionalism for junior doctors. CONCLUSION: Despite concerns raised by professional societies about the erosion of professionalism, participants in this study amply demonstrated the necessary qualities during the recent healthcare crisis. However, there were several examples of strained professional behaviour witnessed by the participants and these examples highlight aspects of medical professionalism that medical educators and professional organisations should address in the future, including the balance between personal safety and duty of care.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".