Understanding what residents want and what residents need: the challenge of cultural training in pediatrics
Bibliographic record
Abstract
Background: There is increasing recognition of the need for sophistication in the way culture is understood and taught in medicine.Method: A two-phase study designed to understand how best to approach cultural training with pediatric residents was conducted. A needs assessment, consisting of resident and faculty focus groups, was carried out from which a workshop was developed for pediatric residents. The aims were to increase knowledge of local cultures and resources as well as to encourage self-reflection and awareness of cultural issues.Results: Focus group participants were consistent in identifying needs for training in: (1) a specific knowledge base of local cultural groups; (2) skills to better negotiate cultural encounters; (3) reconciling general cultural knowledge with an understanding of individual patient/family beliefs and practices. Analysis of focus group and workshop data suggests that culture is seen as both an obstacle and challenge. Cultural training in medicine uncovers a clash of epistemologies: the promotion of culturally-centered medicine is ‘strange’ to learners situated within a pedagogical tradition based on a ‘familiar’ reductionistic view of health.Conclusion: Reconciling these divergent epistemologies requires a paradigm shift in how medicine understands culture and cultural training. These findings raise questions for consideration in other residency programs.Practice points Residents and teaching faculty identify culturally sensitive care as a challenge in medical pratice.Residents and faculty identify a specific knowledge base of local cultural groups’ beliefs and practices as well as skill building as important curricular elements in cultural competency training.Cultural competency educational interventions that presents information on cultural groups’ beliefs and practices may inadvertently lead to increased stereotyping unless also accompanied by a conceptual/anthropological understanding of culture.In order to be successful, curricula must be aware of the “epistemological clash” that occurs when a reductionist view of health meets this conceptual understanding.Training in culturally sensitive care should focus on fostering attitudes, knowledge and skills to negotiate complex transcultural encounters.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".