Striving While Accepting: Exploring the Relationship Between Identity and Implicit Bias Recognition and Management
Bibliographic record
Abstract
PURPOSE: Implicit biases worsen outcomes for underserved and marginalized populations. Once health professionals are made aware of their implicit biases, a process ensues where they must reconcile this information with their personal and professional identities. The authors sought to explore how identity influences the process of implicit bias recognition and management. METHOD: Using constructivist grounded theory, the authors recruited 11 faculty and 10 resident participants working at an academic health science center in Canada. Interviews took place from June to October 2017. Participants took an online version of the mental illness implicit association test (IAT) which provides users with their degree of implicit dangerousness bias toward individuals with either physical or mental illness. Once they completed the IAT, participants were invited to draw a rich picture and interviewed about their picture and experience of taking their IAT. Data were analyzed using constant comparative procedures to develop focused codes and work toward the development of a deeper understanding of relationships among themes. RESULTS: Once implicit biases were brought into conscious awareness, participants acknowledged vulnerabilities which provoked tension between their personal and professional identities. Participants suggested that they reconcile these tensions through a process described as striving for the ideal while accepting the actual. Relationships were central to the process; however, residents and faculty viewed the role of relationships differently. CONCLUSIONS: Striving for self-improvement while accepting individual shortcomings may provide a model for addressing implicit bias among health professionals, and relational dynamics appear to influence the process of recognizing and managing biases.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".