Implicit Bias in Health Professions: From Recognition to Transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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 it