Not all ‘impostors’ are created equal: A dimensional, person-centered, and theory-based analysis of medical students
Bibliographic record
Abstract
PURPOSE: Research on the impostor phenomenon (IP) is rapidly growing in medical education due to its relationship with distress and burnout. How IP is theoretically conceptualized and analyzed has been inconsistent, however, which limits our understanding of results and how to act on them. We hypothesized that a person-centered analysis, in combination with a robust theoretical framework, would provide a more specific 'profile' of medical student IP and help to optimize supports for their well-being. MATERIALS & METHODS: We used exploratory factor analysis to assess the factor structure of the Clance Impostor Phenomenon Scale (CIPS) in medical students, followed by cluster analysis to identify distinct 'impostor' profiles, based on the identified factors. We then used self-determination theory's (SDT) framework of motivation to explore how students in each profile differed in their general causality orientation, autonomous motivation towards going to medical school, and psychological need satisfaction in the medical program - factors that SDT identifies as predictors of engagement, performance, and well-being. RESULTS: Factor analysis yielded three main IP factors - feeling like a fake, attributing success to luck, and discounting achievement - in line with Clance's original definition of IP. The cluster analysis then identified four distinct IP profiles based on individual differences in these factors, each varying in aspects of their self-determination. CONCLUSIONS: This study sheds light on the ways that medical students may experience IP, further reinforcing the notion that not all 'impostors' are created equal. Findings support the three-factor structure of the CIPS among medical students, and that most students will fall into one of four IP profiles. These profiles and their implications are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.013 | 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".