Cultural models within general practice training: a scoping review
Bibliographic record
Abstract
BACKGROUND: Doctors training to become GPs (GPs-in-training) are increasingly working in cross-cultural consultations. Cultural models have been developed as frameworks to better equip medical professionals towards more culturally appropriate health care, with potential to improve equity in healthcare systems. AIM: To map evidence on models of cultural competence, cultural safety, cultural humility, and transcultural care within GP training worldwide. DESIGN & SETTING: A scoping review was conducted using Arksey and O'Malley's framework. METHOD: Searches were conducted across three databases, extending to grey literature such as curricula. Articles were extracted, reviewed, and analysed according to inclusion criteria. RESULTS: Nineteen articles met inclusion criteria. Publications ranged from 2008-2024, with 10 articles from Australia, five from the US, two from Sweden, one from Canada, and one from The Netherlands. The following three themes were generated: unlearning; informal learning; and informed learning. The literature illustrates that there are gaps in knowledge of what the models are and how best to practise and teach them within GP education. CONCLUSION: Cultural models advocate for cultural awareness, examine power imbalances, and encourage self-reflexivity and learning. Integrating cultural models into health care can better serve all patients, with potential to reduce health inequities. There also needs to be an adaptation to learning in traditional GP consultations with a focus on how our own biases impact the care that we provide, and a more formal learning of cultural models best delivered by GP trainers in partnership with cultural mentors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".