Guilt and Burnout in Medical Students
Bibliographic record
Abstract
THEORY: Burnout is prevalent among medical students and is correlated with negative feelings, behaviors, and outcomes. Empathy is a desired trait for medical students that has been correlated with reduced burnout. The concept of guilt is closely related to concern about the well-being of others; therefore, feelings of guilt may be associated with empathy. Excessive guilt poses an increased risk for internalized distress, symptoms such as anhedonia, and may be related to burnout. The relationship between pathogenic guilt and burnout in medical students is unknown. HYPOTHESIS: We hypothesize that pathogenic guilt is present and related to both burnout and empathy in medical students. METHODS: We conducted a cross-sectional survey study of all students in one medical school. Data were collected in February 2020. The Oldenburg Burnout Inventory (OBLI), Toronto Empathy Questionnaire (TEQ), and Interpersonal Guilt Questionaire-67 (IGQ-67) were used. A modified version of IGQ-67 was used to measure four subscales of pathogenic guilt: survival guilt, separation guilt, omnipotence guilt, and self-hate guilt. Data analyses for this study including screening, evaluation of assumptions, descriptive statistics, reliabilities, one-way ANOVA, and correlation coefficients, were conducted using SPSS version 26. RESULTS: Of 300, 168 (56.0%) students participated in the study. Survival, omnipotence, and self-hate classes of pathogenic guilt were positively correlated with burnout. Empathy was correlated with two classes of pathogenic guilt: survival and omnipotence. Empathy was inversely related to burnout (disengagement). CONCLUSIONS: Pathogenic guilt may be a contributor to burnout in medical students. Guilt should be a target of prevention and treatment in burnout in medical students. Supplemental data for this article is available online at https://doi.org/10.1080/10401334.2021.1891544.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".