Recalibrating Self‐Assessment: Navigating Imposter Syndrome Through Metacognitive Reflection
Bibliographic record
Abstract
BACKGROUND: Accurate self-assessment is foundational for life-long learning, professional development and patient safety, yet many learners struggle to develop this fundamental skill. Even skilled self-assessors-or savvy calibrators-may sometimes struggle with self-assessment accuracy, particularly during professional transitions and challenges. This study explored the metacognitive processes employed by high-performing physicians to maintain or recalibrate accurate self-assessment across diverse professional contexts. METHODS: Former chief residents, who we defined as high-performing physicians by virtue of earning the chief role via a competitive application and vetting process, were purposefully sampled. Semistructured interviews were used to explore participants' experiences regarding self-assessment accuracy, imposter syndrome and managing feelings of self-doubt. The study employed Braun and Clarke's reflexive thematic analysis. FINDINGS: The findings reveal that all 10 participants effectively recalibrated their self-assessments when confronted with imposter syndrome by incorporating metacognitive reflection, feedback and emotional awareness. The metacognitive reflection work they carried out to confront imposter syndrome harnessed a growth mindset, but participants cautioned that this orientation needed to be adopted in moderation. CONCLUSION: Findings provided valuable insights and strategies for individuals grappling with imposter syndrome, a prevalent issue in medicine, particularly among high performers. This study highlights the potential for enhancing professional development and well-being by fostering self-assessment skills through metacognitive reflection to constructively adopt a growth mindset to overcome imposter syndrome. While feedback seeking could support calibration, our findings revealed that an excessive focus on growth mindset can shift from productive to counterproductive-creating a risky cycle of self-doubt and overcorrection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".