The Actual Versus Idealized Self: Exploring Responses to Feedback About Implicit Bias in Health Professionals
Bibliographic record
Abstract
PURPOSE: Implicit bias can adversely affect health disparities. The implicit association test (IAT) is a prompt to stimulate reflection; however, feedback about bias may trigger emotions that reduce the effectiveness of feedback interventions. Exploring how individuals process feedback about implicit bias may inform bias recognition and management curricula. The authors sought to explore how health professionals perceive the influence of the experience of taking the IAT and receiving their results. METHOD: Using constructivist grounded theory methodology, the authors conducted semistructured interviews with 21 pediatric physicians and nurses at the Schulich School of Medicine and Dentistry, Western University, Ontario, Canada, from September 2015 to November 2016 after they completed the mental illness IAT and received their result. Data were analyzed using constant comparative procedures to work toward axial coding and development of an explanatory theory. RESULTS: When provided feedback about their implicit attitudes, participants described tensions between acceptance and justification, and between how IAT results relate to idealized and actual personal and professional identity. Participants acknowledged desire for change while accepting that change is difficult. Most participants described the experience of taking the IAT and receiving their result as positive, neutral, or interesting. CONCLUSIONS: These findings contribute to emerging understandings of the relationship between emotions and feedback and may offer potential mediators to reconcile feedback that reveals discrepancies between an individual's actual and idealized identities. These results suggest that reflection informed by tensions between actual and aspirational aspects of professional identity may hold potential for implicit bias recognition and management curricula.
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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 | 0.034 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".