The Prevalence of Imposter Phenomenon in (Post)graduate Medical Education
Bibliographic record
Abstract
Abstract Phenomenon Imposter phenomenon (IP) is the feeling of inadequacy despite demonstrating external standards of success. Few studies have broadly examined the prevalence of IP in resident-physicians. This study assessed the prevalence of self-reported IP in resident-physicians, exploring the correlation of demographic risk factors and feelings of IP. Approach All residents, across all years of training and programs at McMaster University during the 2019-2020 academic year were recruited to complete a self-report survey. Survey items gathered demographic information and measured self-reported feelings of IP and Clance Imposter Phenomenon Scale (CIPS) scores. Findings 519 out of 977 (53.1%) individuals completed the survey. Measured by the CIPS, clinically significant IP occurred in 59.2% (n=307) of participants. After completing the CIPS, participants self-reported feelings of low (25.0%, n=130), medium (41.9%, n=218), high (19.0%, n=99), and intense (3.7%, n= 19) IP. 64.9% (n=337) of respondents felt they hid feelings of IP during residency. 62.4% (n=324) of respondents were unaware of resources available to them as they struggled with feelings of IP. Only female gender was associated with IP (p <0.001). Insights IP is highly prevalent across a broad range of residents, independent of clinical discipline and most demographics. Educators and administrators should attend to IP by normalizing the discussion of IP and ensuring adequate access to resources for support.
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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 | 0.011 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".