Implicit Bias in Health Professions: From Recognition to Transformation
Bibliographic record
Abstract
Implicit bias recognition and management curricula are offered as an increasingly popular solution to address health disparities and advance equity. Despite growth in the field, approaches to implicit bias instruction are varied and have mixed results. The concept of implicit bias recognition and management is relatively nascent, and discussions related to implicit bias have also evoked critique and controversy. In addition, challenges related to assessment, faculty development, and resistant learners are emerging in the literature. In this context, the authors have reframed implicit bias recognition and management curricula as unique forms of transformative learning that raise critical consciousness in both individuals and clinical learning environments. The authors have proposed transformative learning theory (TLT) as a guide for implementing educational strategies related to implicit bias in health professions. When viewed through the lens of TLT, curricula to recognize and manage implicit biases are positioned as a tool to advance social justice.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".