Let’s Talk about Bias in Healthcare: Experiences from an Interactive Interprofessional Student Seminar
Bibliographic record
Abstract
Background: Education to increase awareness of the impact of bias in healthcare should be included in all health professions training programs. This report describes the implementation and outcomes of an interactive, interprofessional pilot seminar on racial bias in healthcare for health professions students. Methods: Forty students across the University of Nebraska Medical Center’s six health profession colleges participated in a 3-part, 1-hour seminar, including a video vignette depicting examples of bias in the hospital, facilitated interprofessional small group discussions, and interaction with a health equity expert panel. We analyzed the results of participants’ Ethnic Perspective-Taking (EP) and Implicit Bias Knowledge scale (IBKS) scores before and after the seminar. Results: There was a statistically significant increase (p<0.001) in the average post-seminar EP scores (30.6 post-seminar vs 27.8 pre-seminar). For the adapted IBKS, there were significant improvements in participant knowledge, skills to identify, and ability to explain the impact of implicit biases (p<0.05). Participants highlighted the importance of including education about bias in healthcare training, and some suggested mandatory education. All facilitators agreed that learners gained a deeper appreciation for the effect of bias and racism on health outcomes and participants understood how bias and racism affect patient care and clinician experience after the seminar. Conclusion: Health professions training often lacks integrated interprofessional and health equity education. This seminar addresses both, engaging community voices without heavy resources. Despite low participation, results show the benefits of interactive sessions on health equity, helping students grasp their role in equitable care and influencing future practice.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".