Infusing Diversity and Equity Into Clinical Teaching: Training the Trainers
Bibliographic record
Abstract
Clinical instructors in health care disciplines are charged with engaging students in experiential learning wherein respect and cultural sensitivity is applied. This article reports on the results of 3 diversity workshops conducted for clinical preceptors and field instructors from various disciplines. The workshops were developed in response to students' growing concerns that their academic learning experiences were negatively affected by dissatisfying management of differences between students, faculty, and preceptors with respect to ethno-racial group membership, socioeconomic level, and degree of privilege and power. The workshops included a didactic session that presented basic principles of social and health equity followed by small-group reflection about various ethical and moral dilemmas that were presented in clinical education scenarios. Examples of discrimination on a variety of levels were addressed in these workshops, including race, ethnicity, immigration status, sexual orientation, religion, body size and appearance, ability, age, socioeconomic class, religious faith, and gender. The group exercises and discussion from these sessions provided valuable insight and approaches to difficult but common areas of discomfiture encountered in the clinical teaching setting. This article presents the findings from participants of these diversity workshops in order to encourage the application of equity principles into clinical teaching in midwifery and other health care education contexts.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.027 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".