Barriers and Facilitators for Shared Decision Making in Breast Reconstruction among Stakeholders in the Chinese Context: A Qualitative Study
Bibliographic record
Abstract
Objective. This qualitative study explores the barriers and facilitators to implementing shared decision making (SDM) for breast reconstruction (BR) from multistakeholder perspectives in the Chinese health care context. Methods. A qualitative study was conducted from November 2021 to January 2022, involving 36 participants, including patients, doctors, nurses, and hospital administrators from 3 tertiary hospitals in Beijing, Hebei, and Guangzhou. Purposeful and snowball sampling was used until data saturation. In-depth semi-structured interviews were analyzed using thematic analysis. Results. Findings from 36 stakeholders (20 patients, 16 health care providers/administrators) revealed 5 key dimensions influencing SDM implementation: decision making, patient, health care professional (HCP), organizational, and societal levels. Notable factors include patient self-efficacy, information needs, HCPs’ role recognition and SDM competencies, team coordination, SDM convenience, availability of support tools, and cultural influences. Limitations. The limitations of this study primarily stem from the narrow sample source, which includes only 3 regions in mainland China. Conclusion. Successful SDM implementation in China requires optimizing clinical workflows, utilizing technological tools, providing professional training, and integrating SDM with traditional Chinese medicine philosophies. These strategies enhance decision-making quality and align SDM practices with Chinese cultural values. Practice Implications. Integrating culturally sensitive SDM into clinical workflows, supported by decision tools, training, and robust policies, is essential for BR SDM in China. Highlights Identified barriers and facilitators on shared decision making for breast reconstruction from multistakeholder perspectives in China’s health care context. Explored cultural influences on shared decision making for breast reconstruction in Chinese patients. Emphasized the importance of integrating shared decision making into existing clinical workflows. Proposed integrating traditional Chinese medicine diagnostics with shared decision making for culturally sensitive care.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".