Qualitative analysis of medical student reflections on the implicit association test
Bibliographic record
Abstract
INTRODUCTION: Health professions educators use the Implicit Association Test (IAT) to raise awareness of implicit bias in learners, often engendering strong emotional reactions. Once an emotional reaction ensues, the gap between learner reaction and strategy identification remains relatively underexplored. To better understand how learners may identify bias mitigation strategies, the authors explored perspectives of medical students during the clinical portion of their training to the experience of taking the IAT, and the resulting feedback. METHODS: Medical students in Bronx, NY, USA, participated in one 90-minute session on implicit bias. The focus of analysis for this study is the post-session narrative assignment inviting them to take the race-based IAT and describe both their reaction to and the implications of their IAT results on their future work as physicians. The authors analysed 180 randomly selected de-identified essays completed from 2013 to 2019 using an approach informed by constructivist grounded theory methodology. RESULTS: Medical students with clinical experience respond to the IAT through a continuum that includes their reactions to the IAT, acceptance of bias along with a struggle for strategy identification, and identification of a range of strategies to mitigate the impact of bias on clinical care. Results from the IAT invoked deep emotional reactions in students, and facilitated a questioning of previous assumptions, leading to paradigm shifts. An unexpected contrast to these deep and meaningful reflections was that students rarely chose to identify a strategy, and those that did provided strategies that were less nuanced. CONCLUSION: Despite accepting implicit bias in themselves and desiring to provide unbiased care, students struggled to identify bias mitigation strategies, a crucial prerequisite to skill development. Educators should endeavour to expand instruction to bridge the chasm between students' acceptance of bias and skill development in management of bias to improve the outcomes of their clinical encounters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.514 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".