Evaluating Primary Health Care Performance from User Perspective in China: Review of Survey Instruments and Implementation Issues
Bibliographic record
Abstract
This review aims to summarize the progress of patient evaluation studies focusing on primary health care (PHC) in China, specifically in relation to survey instruments and implementation issues. Eligible studies published in English or Chinese were obtained through online searches of PubMed and China National Knowledge Infrastructure. A descriptive reporting approach was used due to variations in the measurements and administration methods between studies. A total of 471 articles were identified and of these articles; of those 91 full-text articles were included in the final analysis. Most studies used author-developed measurements with five-point Likert response scales and many used the Chinese translations of validated tools from other countries. Most instruments assessed the physical environment, medical equipment, clinical competency and convenience aspects of PHC using a satisfaction rating instead of care experience reporting. Many studies did not report the sampling approach, patient recruitment procedures and survey administration modes. The patient exit survey was the most commonly used survey implementation method. The focus on the structural dimensions of PHC, inconsistent wording, categories of response options that use satisfaction rating, and unclear survey implementation processes are common problems in patient evaluation studies of PHC in China. Further studies are necessary to identify population preferences of PHC in China in order to move towards developing Chinese value-based patient experience measurements.
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 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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".