Interventions addressing the impostor phenomenon: a scoping review
Bibliographic record
Abstract
Introduction: The Impostor Phenomenon (IP) refers to a psychological experience characterized by unjustified feelings of intellectual and professional fraud, accompanied by the fear of not maintaining performance and of being exposed. IP is receiving increasing attention in the fields of psychological health at work and occupational psychology as well as among the general public, since it affects the functioning of both individuals and organizations. The aim of this scoping review is to map the range of interventions that have been conducted to address IP among individuals experiencing it in a professional context. Methods: The search and selection process to identify relevant reports was conducted using the PRISMA-ScR methodology and JBI recommendations and resulted in the selection of 31 studies. Results: The results reported concerning the characteristics of the studies, the interventions described, and the effects identified are heterogeneous. More than half of the studies used research designs (experimental, pre-experimental, exploratory, etc.). Two major types of intervention emerge: training and counseling. The effectiveness of the interventions varies according to the evaluation methodology that was used, although most authors conclude that the proposed intervention is relevant. Discussion: In light of these results, recognizing and educating individuals regarding the various manifestations of IP, as well as offering support in a group context, appear to be primary intervention levers. Future intervention proposals should explore psychosocial and educational influences as well as the impact of the immediate environment on IP-related beliefs.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".