BOS1b.001 Supporting clinicians to adopt a culturally considerate approach to advance care planning conversations with patients
Bibliographic record
Abstract
Background Disparities exist in ACP engagement rates among culturally diverse communities. To address this, we developed an online learning course to help healthcare providers apply a culturally considerate and safe approach and appropriate communication skills when conducting ACP conversations with culturally diverse patients, particularly patients with a Chinese or South Asian background. Methods The course was developed using information from literature reviews, and focus groups with members of the Chinese or South Asian communities. The target audience is any healthcare provider that may have ACP conversations with their patients. Basic ACP information and cultural safety knowledge are considered prerequisites. Three modules were developed: Evolution of ACP: a refresher module about ACP. Culturally Safe Care: how culture can impact care, with an overview of cultural safety. This module applies to all cultures. Culturally safe ACP with Chinese and South Asian communities: bringing together the lessons of the previous two modules in the context of ACP with members of Chinese and South Asian communities. The course is evaluated through a survey following course completion. Results Healthcare providers that completed the evaluation survey were predominantly Nurses (42%) and Social Workers (36%) and did not identify with either the South Asian or Chinese Community (73%). Over 90% of respondents agreed the course was clear and well written, the content was easy to understand, it met their learning needs, and increased their knowledge on how to conduct culturally safe ACP. 97% of respondents thought the information could be applied in practice, and 85% said they are likely to recommend the course to a friend/colleague. Respondents requested expansion to cover additional cultures. Conclusion Using the learnings from the course in ACP conversations, healthcare providers can help their culturally diverse patients to get care aligned with their values, beliefs and wishes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.273 | 0.079 |
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".