The impostor phenomenon in post‐secondary students: A review of the literature
Bibliographic record
Abstract
Abstract The impostor phenomenon (IP) is characterised by a belief that one is not actually intelligent or accomplished, despite objective evidence of success, but rather has fooled others and is therefore vulnerable to discovery as a fraud. Impostor feelings are widespread in the post‐secondary population and may be disproportionately experienced by groups already marginalised within academia (e.g., women, racialised students). The IP may be associated with significant mental health issues and changes long‐term education and career plans; as such, it is imperative that administrators take steps to address impostorism on university and college campuses. The purpose of this review was to summarise factors of the IP from existing research on the experiences and background characteristics of undergraduate, graduate and professional students (i.e., medicine, law). Findings on the subject of IP and gender as well as IP and ethnicity were inconsistent. Most studies found that IP was negatively related to self‐esteem and various dimensions of mental health. The relationship of IP to grade point average was inconsistent but several studies showed a negative association between IP and measures of academic performance. These findings suggest there is a critical need to bring awareness to and address experiences of the IP in the academic community; implications for campus mental health professionals are explored. Limitations of existing research are addressed and directions for future research are discussed. Context and implications Rationale for this study To offer a summary of findings related to experiences of the IP among students in higher education. Why the new findings matter IP is negatively associated with multiple mental health concerns, self‐esteem, and measures of academic performance; therefore, interventions are needed to better support students in their academic pursuits. Implications for higher education personnel (e.g., professors, administrators) Implications for mental health professionals and students are discussed. Recommendations for intervention include raising awareness and normalising feelings of IP through workshops and orientations, and resource development with a particular focus on practical strategies to support students in coping with feelings of IP. Earlier intervention to manage IP is needed to curb associated mental health issues and deleterious changes to long‐term education and career plans.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".