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S1493 Impostor Phenomenon and Microaggression Education and Self-Assessment in Female Healthcare Professionals in the Field of Gastroenterology

2022· article· en· W4316084636 on OpenAlexaboutno aff
Natalie Farha, Aline Charabaty, Anita Afzali, Carol A. Burke

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCompetence (human resources)FeelingDemographicsScale (ratio)Health professionalsHealth careFamily medicineMedical educationPsychologySocial psychology

Abstract

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Introduction: Many high-achieving individuals, despite contrary evidence, believe their successes are due to luck rather than their own competence. This experience is termed “impostor phenomenon (IP).” IP and another experience, called micro-aggressions (MA), in the workplace may limit an individual’s ability to achieve career goals and professional satisfaction. A lecture was provided at a womens leadership conference to raise awareness about the prevalence of and strategies to mitigate IP and MA. Herein, we describe attendees’ assessment of their understanding of and exposure to IP and MA in order to understand the prevalence of these phenomena in female healthcare professionals and to guide future educational programming. Methods: Attendees were invited to complete an anonymous survey for self-reflection, including demographic information, experience with IP and micro-aggressions, and completion of the Clance IP Scale, a validated survey assessing the level of IP characteristics. Surveys were collected at the conclusion of the lecture on IP and MA. We report group demographics, prevalence of IP and MA, and factors associated with level of IP. Demographic data is reported as percentages and survey scores reported as means. Results: 86/132 (65%) attendees completed the survey and 97.7% were female. (Table 1) 50% were practicing gastroenterologists, 21.3% GI fellows, 13.8% medical residents, 6.3% advanced practice providers, and 1.3% medical students. 88.2% reported having experienced feelings of IP and 92.7% reported being a target of MA. The mean Clance IP scale score was 66.8 (ranging from 39 to 91). Only 1.2% of respondents reported few IP experiences while 69% scored in the frequent to intense feelings of IP. Black, younger, and medical trainee respondents had the highest IP scores compared to practicing gastroenterologists, advanced practice providers, and other respondents. IP scale scores decreased with age (Figure 1). Conclusion: Exposure to IP and MA is experienced almost universally by female healthcare professionals in the field of gastroenterology. IP is quantified as frequent and severe in nearly 3 of every 4 respondents. Based on our survey results, future educational programming to increase awareness of these issues and provide solutions to addressing them should be tailored towards early identification and intervention in trainees and young professionals as well as creating inclusive work environments that address systemic bias and fostering a work culture that supports diversity. Table 1. - Demographic Information and Clance Impostor Phenomenon Scale Scoring Demographics Age (N = 86) N (%) 25-30 15 (17.4%) 31-35 24 (27.9%) 36-40 19 (22.1%) 41-45 12 (14.0%) 45-50 10 (11.6%) 51-55 4 (4.7%) 56 and older 2 (2.3%) Gender (N = 86) Female 84 (97.7%) Male 2 (2.3%) Ethnicity (N = 86) Black or African American 2 (2.3%) Asian 33 (38.4%) White 41 (47.7%) Hispanic or Latino 5 (5.8%) Prefer not to answer 5 (5.8%) Current employment status (N = 80) Medical student 1 (1.3%) Medical resident 11 (13.8%) GI fellow 17 (21.3%) Practicing gastroenterologist 40 (50.0%) Advanced practice provider 5 (6.3%) Other 6 (7.5%) Practicing gastroenterologist years in practice (N = 39) 1-5 17 (43.6%) 6-10 9 (23.1%) 11-15 7 (17.9%) 16-20 3 (7.7%) 21 and more 3 (7.7%) Have you ever had feelings of Impostor Phenomenon? (N = 85) Yes 75 (88.2%) No 4 (4.7%) I don’t know 6 (7.1%) Have you ever been a target of micro-aggression? (N = 82) Yes 76 (92.7%) No 1 (1.2%) I don’t know 5 (6.1%) Clance Impostor Phenomenon Scale* Mean score (range) (N = 86) 66.8 (39 - 91) Number (%) of respondents scoring in each category Score < 40: few IP experiences/feelings 1 (1.2%) Score 41 – 60: moderate IP feelings/experiences 26 (30.2%) Score 61 – 80: frequently IP feelings/experiences 45 (52.3%) Score > 80: often has intense IP feelings/experiences 14 (16.3%) Mean score by age (N = 86) 25-30 (N = 15) 72.3 31-35 (N = 24) 70.1 36-40 (N = 19) 66.9 41-45 (N = 12) 63.6 45-50 (N = 10) 61.9 51-55 (N = 4) 56.5 56 and older (N = 2) 47.5 Mean score by ethnicity (N = 86) Black or African American (N = 2) 81.5 Asian (N = 33) 68.2 White (N = 41) 64.8 Hispanic or Latino (N = 5) 64 Prefer not to answer (N = 5) 70 Mean score by employment status (N = 80) Medical student (N = 1) 71 Medical resident (N = 11) 73.6 GI fellow (N = 17) 73.7 Practicing gastroenterologist (N = 40) 64.9 Advanced practice provider (N = 5) 54.6 Other (N = 6) 61.5 Mean score by practicing gastroenterologist years in practice (N = 39) 1-5 (N = 17) 68.3 6-10 (N = 9) 60.3 11-15 (N = 7) 68.9 16-20 (N = 3) 56 21 and more (N = 3) 61.3 *Clance Impostor Phenomenon Scale (CIPS). From The Impostor Phenomenon: When Success Makes You Feel Like A Fake (pp. 20-22), by P.R. Clance, 1985, Toronto: Bantam Books. Copyright 1985 by Pauline Rose Clance, Ph.D., ABPP. Use by permission of Dr. Pauline Rose Clance. Do not reproduce/copy/distribute without permission from Pauline Rose Clance, [email protected], www.paulineroseclance.com. Figure 1.: Mean Clance Impostor Phenomenon Scale Score by Age.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.009
GPT teacher head0.345
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Published2022
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