Uncovering cynicism in medical training: a qualitative analysis of medical online discussion forums
Bibliographic record
Abstract
OBJECTIVE: The development of cynicism in medicine, defined as a decline in empathy and emotional neutralisation during medical training, is a significant concern for medical educators. We sought to use online medical student discussion groups to provide insight into how cynicism in medicine is perceived, the consequences of cynicism on medical trainee development and potential links between the hidden curriculum and cynicism. SETTING: Online analysis of discussion topics in Premed101 (Canadian) and Student Doctor Network (American) forums. PARTICIPANTS: 511 posts from seven discussion topics were analysed using NVivo 11. Participants in the forums included medical students, residents and practising physicians. METHODS: Inductive content analysis was used to develop a data-driven coding scheme that evolved throughout the analysis. Measures were taken to ensure the trustworthiness of findings, including duplicate independent coding of a sub-sample of posts and the maintenance of an audit trail. RESULTS: Medical students, residents and practising physicians participating in the discussion forums engaged in discourse about cynicism and highlighted themes of the hidden curriculum resulting in cynicism. These included the progression of cynicism over the course of medical training as a coping mechanism; the development of challenging work environments due to factors such as limited support, hierarchical demands and long work hours; and the challenge of initiating change due to the tolerance of unprofessionalism and the highly stressful nature of medicine. CONCLUSION: Our unique study of North American medical discussion posts demonstrates that cynicism develops progressively and is compounded by conflicts between the hidden and formal curriculum. Online discussion groups are a novel resource to provide insight into the culture of medical training.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".