302 Shared decision-making inhealthcare in mainland china: a scoping review
Bibliographic record
Abstract
Introduction Shared decision-making (SDM) facilitates the participation of healthcare professionals and patients in treatment decisions. We conducted a scoping review to assess SDM’s current status in mainland China, referencing the Ottawa Decision Support Framework (ODSF). Methods Our review encompassed extensive searches across six English and four Chinese databases, and various gray literature until April 30, 2021. Results were synthesized using thematic analysis. Results Out of the 60 included studies, we identified three key themes based on the ODSF framework: decisional needs, decision support, and decisional outcomes. However, there appears to be a lack of comprehensive understanding of concepts related to decisional needs in China. Only a few studies have delved into feasibility, preference, choice, and outcome factors in the SDM process. Another challenge emerges from an absence of uniform standards for developing patient decision aids (PDAs). Furthermore, regarding health outcome indicators, their predominant focus remains on physiological needs. Discussion This study demonstrates the potential of SDM in mainland China to enhance the quality and efficiency of medical services. Although there has been some progress, it is important to note that SDM is still in its early stages of development in mainland China. Therefore, it is crucial to allocate more attention and resources towards understanding the decisional needs, providing adequate support, and evaluating the outcomes of SDM implementation. Conclusion lt is important to explore the concept and expression of decisional needs in the context of Chinese culture. Subsequent studies should focus on constructing a scientifically rigorous and systematic approach for the development of PDAs, and considering the adaptation of SDM steps to the clinical context in China during SDM implementation. Concurrently, The focus on health outcomes in Chinese SDM studies, driven by the unique healthcare resource landscape, underscores the necessity of prioritizing basic needs within limited resources.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".