Unhealthy Communication: Health Care Communication Between Majority and Minority Cultural Groups
Bibliographic record
Abstract
Effective communication is important in almost every aspect of life. Within the medical setting, effective communication is paramount because the consequences of miscommunication can be deadly. In health care contexts interpersonal communication is not only affected by the nature of the interaction but also the power differentials between the people participating. The purpose of this paper is to examine how health care communication is affected by power dynamics. Specifically, it will look at the communication between majority-culture doctors and minority patients in order to advance the following argument: the asymmetrical power relationship between doctors and patients is exacerbated when doctors are from majority cultural backgrounds and patients are from minority cultural backgrounds, this is problematic because communication within a medical context is important for patient satisfaction, recall of information, and outcomes. Following the introduction, the literature review explores some of the trends in medical communication literature; these trends include discussions in the literature about the variables and behaviours that affect doctor-patient communication and the critical approach of research done on intercultural medical communication. The section after the literature review establishes that power gaps exist between doctors and patients as well as majority and minority cultural groups. The following paragraphs discuss the negative effects these power differentials have on the intercultural communication between patients and doctors when it comes to patient satisfaction, information recall, and outcomes, before coming to the conclusion that without clear expressions of their communicative differences the communication between these two groups will be compromised.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".