301 ‘All about the value?’: decisional needs of breast reconstruction for breast cancer patients in the chinese context: a mixed-methods study
Bibliographic record
Abstract
Introduction Understanding the breast cancer (BC) patient‘s decisional needs is critical in helping health care professionals (HCPs) provide support to conduct shared decision-making (SDM) and help women make informed breast reconstruction (BR) decisions that are suitable for their clinical and personal circumstances. Therefore, this study aims to explore BC patients‘ participation in BR decision-making and specific decisional needs, especially the manifestations and causes of decisional conflicts, in China. Methods A mixed-methods study was conducted using triangulation of data from interviews and a questionnaire survey with HCPs and BC patients with BR decision-making experience at 5 Beijing centers. The Ottawa Decision Support Framework guided (ODSF) the qualitative and quantitative data analyses. Results A total of 82.53% of Chinese BC patients would consider BR. Seven themes captured patients‘ BR decisional needs per the ODSF: inadequate support/resources (100%, 58.82%) and knowledge (75%, 52.94%) were most frequently cited. Health beliefs (unclear values) reflected Chinese characteristics. Patients had inadequate knowledge (M=19.99/50, SD=8.67) but positive BR attitudes (M=59.48/95, SD=10.45). Discussion Chinese BC patients show positive attitudes but have lower actual involvement in decision-making. Cultural influences, such as patriarchal norms and Confucianism, shape decision- making. BR decisions for Chinese BC patients are complex and often accompanied by decisional conflicts. Inadequate knowledge and inadequate support and resources contribute to these conflicts, emphasizing the need for culturally tailored information and support to promote SDM. Conclusion HCPs need specialized training in SDM to guide patients in decision-making. It is essential to provide relevant resources and support that are culturally and clinically appropriate for Chinese patients.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".