“Rising to the Level of Your Incompetence”: What Physicians’ Self-Assessment of Their Performance Reveals About the Imposter Syndrome in Medicine
Bibliographic record
Abstract
PURPOSE: Mistakes are ubiquitous in medicine; when confronted by error, physicians may experience anxiety, guilt, and self-doubt. Feedback may be useful for navigating these feelings, but only if it matches a physician's self-assessment; self-doubt and the imposter syndrome are examples of inaccurate self-assessments that may affect receptivity to feedback. The impact of real or imagined underperformance on seemingly competent physicians is poorly understood. This study aimed to develop a deeper understanding to identify strategies to support all physicians who struggle. METHOD: In 2015, 28 physicians were interviewed about their experiences with underperformance. Early in the data collection process, participants spontaneously identified the imposter syndrome as a feature of their experiences; questions about the imposter syndrome were probed in subsequent interviews. RESULTS: Many participants-even those at advanced career stages-questioned the validity of their achievements; progressive independence and career advancement were variably experienced as "rising to the level of your incompetence." Not all participants identified as imposters; the imposter syndrome occurred at the extreme end of a spectrum of self-doubt. Even positive feedback could not buffer participants' insecurities, which participants rarely shared with their colleagues. CONCLUSIONS: Self-doubt variably affects clinicians at all career stages. Frequent transitions may cause a resurgence of self-doubt that may affect feedback credibility. Medical educators must recognize that it is not just the underperforming or failing learners who struggle and require support, and medical culture must create space for physicians to share their struggles.
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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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".