Cultural Adaptation and Pilot Testing of a Basic Palliative Care Curriculum for Practicing Physicians and Nurses in Mainland China
Bibliographic record
Abstract
Background:To meet the growing palliative care (PC) needs of China’s aging population, we culturally adapted and pilot tested an evidence-based basic PC training program for practicing clinicians. Design:Barrera’s framework guided a multistage, surface, and deep structural adaptation of an existing course. We pilot tested the final curricula with 51 participants in September 2022. Participant demographics and postcourse satisfaction survey were descriptively analyzed. Results:A total of 20 nurses and 29 physicians completed the course and instruments. Majority of participants were between 31 and 50 years old (n = 39, 79.6%), female (n = 41, 83.7%), internal medicine trained (n = 30, 61.2%), and worked in tertiary hospitals (n = 47, 95.9). Most participants considered the course quality to be “high” or “very high” (n = 47, 95.9%). Conclusions:Practicing physicians and nurses in mainland China consider this culturally adapted basic PC training to be feasible and acceptable. Future studies should evaluate the effectiveness of PC training and develop strategies to overcome implementation challenges.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".