An identity on guard: the impact of microaggressions on the professional identity formation of residents
Bibliographic record
Abstract
PURPOSE: The development of a strong professional identity in medicine has important consequences for patient care, as proper identity formation impacts a physician's confidence, wellbeing, and performance. In non-medical professions, exposure to discrimination and stigma impacts how individuals construct their professional identity. Our study aims to explore how microaggressions from peers impact the professional identity formation of resident physicians. This work was guided by conceptual frameworks on professional identity formation that included socialization, role modelling, and hierarchical structures. METHODS: We conducted semi-structured interviews with [blinded] residents between July 2021 and November 2022. Participants were recruited utilizing both convenience and snowball sampling of residents who self-identified as having experienced microaggressions. During the iterative data collection, we adopted thematic analysis using open coding to identify overarching themes. RESULTS: We interviewed 17 residents from five specialties. Overall, participants perceived that experiencing microaggressions impacted their sense of belonging in medicine, and had a negative impact on participants' progression in residency due to feelings of perceived incompetence, exhaustion at work, and missed opportunities. Participants also felt like they had to guard the diverse aspects of their identities to mitigate the experience of microaggressions. Barriers in addressing microaggressions included fear of personal and professional repercussions, and a sense of futility that reporting would lead to tangible change. More education about microaggressions, increasing transparency on reporting microaggressions, access to open-minded mentors, and creating a safe space to debrief may help mitigate the negative impacts of microaggressions on professional identity formation. CONCLUSIONS: Our study suggests that microaggressions between peers are a barrier to trainees' socialization into the medical profession, as they lead to feelings of exclusion and exhaustion that impact clinical performance. Education on how to identify, report and respond to microaggressions will help to improve the learning environment for vulnerable trainees.
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 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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".