Descriptive Analysis of Patient Experience in Shanghai Primary and Tertiary Care Settings
Bibliographic record
Abstract
Context: China has recently undertaken a nationwide healthcare reform of primary care for its citizens. The new Ottawa-Shanghai Joint School of Medicine (OSJSM) entered this context of reform by developing family medicine training centres for its students.Objective: This study seeks to understand patients’ demographic, perceptions of family medicine, and alignment of needs and values towards family medicine to inform the creation of these new centres.Study Design: To this end, a culturally and linguistically appropriate patient experience survey was created and administered at two primary (CaoJiaDu and TangQiao Community Health Centers) and at a tertiary care centre (Renji Hospital). The survey consisted of questions on demographics, frequency of healthcare usage, satisfaction of care, barriers to access, prioritized values and perceptions of family medicine. It was administered to 400 patients conveniently sampled to have a balance of primary/tertiary settings.Results: Despite common assumptions that Chinese patients may prefer specialist services, this study revealed a 68.3% preference for General Practitioners (GP) over Specialists. There was also overall agreement and preference for values of continuity, comprehensiveness, and coordination in healthcare.Conclusion: These findings reveal that primary care is present in Shanghai and that the core values of family medicine are desired by a majority of respondents. Further analysis, qualitative corroboration and repeating the study in a wider population may be required for more generalizable conclusions, as this study in its current design was limited by convenience sampling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".