Mixed-method evaluation of a culturally-adapted basic palliative care curriculum for practicing physicians in mainland China
Bibliographic record
Abstract
BACKGROUND: China's aging population will escalate palliative care (PC) needs in the next decade. Scalable and culturally-adapted training is necessary to equip practicing clinicians with essential PC skills. The objective of this study is to evaluate a culturally-adapted basic PC training course in mainland China. METHODS: A total of 29 practicing physicians from Zhejiang Province, China, were selected to participate in an in-person training program that spanned six days. We analyzed pre- and post-course quantitative surveys on knowledge, self-efficacy, and behavior using descriptive statistics. We also thematically analyzed post-course semi-structured participant interviews. RESULTS: The majority of participants were aged 41-50 (51.7%), trained in internal medicine (55.2%), worked at tertiary medical centers (93.1%), and did not have clinical PC experience (65.5%). After the course, participants' PC knowledge (p < 0.01) and self-efficacy (p < 0.01) increased, especially in the domains of PC philosophy and physical symptom management. Although statistically significant, changes in participants' self-perceived behaviors were less profound. Thematic analysis of the participant interviews revealed concordant themes, including recognition of cohesiveness between PC principles with traditional Chinese philosophy, and acquisition of actionable clinical knowledge. Key points that expanded beyond the meta-inferences were: 1) emotional resonance with the teaching team is necessary to create a transformational learning experience; and 2) a longitudinal, relationship-centered mentorship process may aid in participants' implementation of PC skills. CONCLUSION: The results of this study indicate that our culturally adapted PC training can increase practicing physicians' PC knowledge and self-efficacy. Scalable basic PC training should preserve and facilitate emotional resonance between participants and instructors to ensure uptake of PC principles. Efforts to understand and overcome the implementation challenges of new PC champions should also be prioritized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".