Patient views of the good doctor in primary care: a qualitative study in six provinces in China
Bibliographic record
Abstract
BACKGROUND: China has been striving to train primary care doctors capable of delivering high-quality service through general practitioner training programs and family doctor team reforms, but these initiatives have not adequately met patient needs and expectations. In order to guide further reform efforts to better meet patient expectations, this study generates a profile of the good doctor in primary care from the patient perspective. METHODS: Semi-structured interviews were conducted in six provinces (Shandong, Zhejiang, Henan, Shaanxi, Shanxi, Heilongjiang) in China. A total of 58 interviewees completed the recorded interviews. Tape-based analysis was used to produce narrative summaries. Trained research assistants listened to the recordings of the interviews and summarized them by 30-s segments. Thematic analysis was performed on narrative summaries to identify thematic families. RESULTS: Five domains and 18 attributes were generated from the analysis of the interview data. The domains of the good doctor in primary care from the patient perspective were: strong Clinical Competency (mentioned by 97% of participants) and Professionalism & Humanism (mentioned by 93% of participants) during service delivery, followed by Service Provision and Information Communication (mentioned by 74% and 62% of participants, respectively). Moreover, Chinese patients expect that primary care doctors have high educational attainment and a good personality (mentioned by 41% of participants). CONCLUSIONS: This five-domain profile of the good doctor in primary care constitutes a foundation for further primary care workforce capacity building. Further primary care reform efforts should reflect the patient views and expectations, especially in the family physician competency framework and primary care performance assessment system development. Meanwhile, frontline primary care organizations also need to create supportive environments to assist competent doctors practice in primary care, especially through facilitating the learning of primary care doctors and improving their well-being.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".