Sources of distress during medical training and clinical practice: Suggestions for reducing their impact
Bibliographic record
Abstract
BACKGROUND: Medical students and doctors experience several types of professional distress. Their causes ("stressors") are commonly classified as exogenous (adapting to medical school or clinical practice) and endogenous (due to personality traits). Attempts to reduce distress have consisted of providing students with support and counseling, and improving doctors' management of work time and workload. AIM: To review the common professional stressors, suggest additional ones, and propose ways to reduce their impact. METHOD: Narrative review of the literature. RESULTS AND CONCLUSION: We suggest adding two professional stressors to those already described in the literature. First, the incongruity between students' expectations and the realities of medical training and practice. Second, the inconsistencies between some aspects of medical education (e.g., its biomedical orientation) and clinical practice (e.g., high proportion of patients with psychosocial problems). The impact of these stressors may be reduced by two modifications in undergraduate medical programs. First, by identifying training-practice discrepancies, with a view of correcting them. Second, by informing medical students, both upon admission and throughout the curriculum, about the types and frequency of professional distress, with a view of creating realistic expectations, teaching students how to deal with stressors, and encouraging them to seek counseling when needed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".