Ethnocultural influences in how people prefer to obtain and receive health information
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To develop an understanding of south Asian and Chinese people's preferences about where to find health information and how best to receive health information, relative to their white counterparts. BACKGROUND: South Asian and Chinese ethnic groups represent the largest proportion of Canada's growing visible minorities. There may be challenges to ensuring that south Asian and Chinese people have access to health information in the same way that others do. DESIGN: Qualitative descriptive. METHODS: Fifty-two participants (12 white, 16 south Asian and 24 Chinese) engaged in six focus groups (two for each ethnocultural group). Focus groups were conducted in English, Punjabi and Cantonese, with the assistance of Punjabi and Cantonese interpreters. Questions were focused on how participants have preferred or would prefer to receive health information (e.g., when, where, what format, from whom), as well as the facilitators and barriers to understanding the health information. RESULTS: Participants agreed that although physicians were their primary source for health information, they also used written materials, media and the Internet to glean information. Participants identified concerns regarding the use of technical jargon by healthcare providers. South Asians and Chinese referred to their English language fluency and the lack of ethnoculturally specific information as additional challenges to understanding information they were offered. Whether and how family members were included in the communication process, also varied by ethnocultural group. CONCLUSIONS: As Canada welcomes immigrants from other countries, and its population becomes more diverse, healthcare providers need to have an understanding of the potential diversity in how to approach offering health information. RELEVANCE TO CLINICAL PRACTICE: Healthcare providers need to consider what people of different ethnocultural backgrounds need when developing effective health communication strategies.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".