The Reliability and Validity of Chinese Version of JSPE‐HP Among Doctors From Primary‐Level Clinics: A Cross‐Sectional Study
Bibliographic record
Abstract
Background and Aims: Primary-level clinics play an important role in providing healthcare services and maintaining citizens' health in China. However, there is limited research on empathy among Chinese doctors in primary care settings. We will translate the Jefferson Scale of Physician Empathy for physicians and health professional (JSPE-HP) into Chinese and evaluate its reliability and validity among doctors from primary-level clinics and the condition of doctor empathy will also be evaluated. Methods: Two research assistants, fluent in both English and Chinese, were invited to evaluate our translation of the JSPE-HP. From April to July in 2023, a cross-sectional survey was then administered to a convenience sample of 459 doctors from primary-level clinics in Zhejiang Province, China. The quality of the scale was tested using internal consistency reliability, composite reliability (CR), and convergent and discriminant validity. Results: The Chinese version of the JSPE-HP scale comprises 20 items across three dimensions. The scale had an overall Cronbach's α of 0.85, and the CRs of the three dimensions were 0.88, 0.81 and 0.74 respectively, indicating satisfactory reliability. Average variance extracted ranged from 0.43 to 0.59 and the Chi-squared difference between the constrained and free models was significant, indicating convergent and discriminant validity. The mean total score for the doctors was 110.52 (SD = 15.80). It was 75.43 after transformation. Conclusion: The reliability and validity of the scale is acceptable, and it could serve as an evaluation tool for doctors of Chinese background. The empathy level of doctors from primary-level clinics should be improved further.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".