Supportive Care: Communication Strategies to Improve Cultural Competence in Shared Decision Making
Bibliographic record
Abstract
Historic migration and the ever-increasing current migration into Western countries have greatly changed the ethnic and cultural patterns of patient populations. Because health care beliefs of minority groups may follow their religion and country of origin, inevitable conflict can arise with decision making at the end of life. The principles of truth telling and patient autonomy are embedded in the framework of Anglo-American medical ethics. In contrast, in many parts of the world, the cultural norm is protection of the patient from the truth, decision making by the family, and a tradition of familial piety, where it is dishonorable not to do as much as possible for parents. The challenge for health care professionals is to understand how culture has enormous potential to influence patients' responses to medical issues, such as healing and suffering, as well as the physician-patient relationship. Our paper provides a framework of communication strategies that enhance crosscultural competency within nephrology teams. Shared decision making also enables clinicians to be culturally competent communicators by providing a model where clinicians and patients jointly consider best clinical evidence in light of a patient's specific health characteristics and values when choosing health care. The development of decision aids to include cultural awareness could avoid conflict proactively, more productively address it when it occurs, and enable decision making within the framework of the patient and family cultural beliefs.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".