Rethinking the impostor phenomenon: An umbrella review of concept, context and interventions
Bibliographic record
Abstract
BACKGROUND: Impostor Phenomenon (IP) is a psychological experience characterized by persistent self-doubt and an inability to internalize achievements, leading to negative emotional and professional consequences. In health professions education (HPE), IP is of particular concern because it has been linked to learner well-being, professional development and potentially to patient safety. Despite growing interest in IP within health care and academic settings, uncertainties remain about how IP is conceptualized, the contextual factors that shape its occurrence and the effectiveness of interventions to address it. This umbrella review synthesizes existing systematic and scoping reviews to examine (1) IP's conceptualization, (2) the contextual influences associated with its occurrence and (3) the effectiveness of interventions aimed at reducing its impact. METHODS: This umbrella review is reported following the PRISMA-ScR guidelines. A comprehensive literature search was conducted in Medline, Embase, PubMed, PsycINFO, Scopus, Web of Science, CINAHL, Cochrane Library and ERIC from November 27th to December 4th, 2024. Systematic and scoping reviews on IP and related interventions were included. Methodological quality was assessed using AMSTAR-2 and the Joanna Briggs Institute (JBI) Critical Appraisal Tool. RESULTS: Sixteen reviews (eight systematic and eight scoping) covering diverse populations and methodologies were included. The reviews revealed a lack of conceptual consistency and challenges in measuring and comparing IP, with no gold-standard assessment tool. Reported prevalence varied widely- reflecting conceptual and measurement challenges- with risk factors including perfectionism, marginalized status and hierarchical workplace cultures. Coaching, online self-study modules and mindfulness-based interventions showed potential in reducing IP intensity, but the methodological quality of intervention studies was inconsistent, with few high-quality studies. CONCLUSIONS: Results highlight the need for conceptual refinement, standardized measurement tools and rigorous intervention research on IP. Future studies should focus on deconstructing and reconceptualizing IP, as well as developing multidimensional assessment frameworks and evaluating evidence-based interventions to improve confidence and competence among individuals experiencing IP.
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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 | 0.051 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".