Decisional Conflict, Patient Involvement, and the Associated Psychological Factors Relating to Mastectomy Decisions Among Women With Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Most women with breast cancer in China have received a mastectomy despite emerging breast-conserving alternatives. Their decision-making relating to mastectomy is unclear. OBJECTIVE: To investigate decisional conflict, women's involvement, and psychological factors relating to mastectomy decisions. METHODS: Women with breast cancer 18 years and older who had a mastectomy were recruited from 2 hospitals in China. A conceptual framework adapted from the Ottawa Decisional Support Framework was used to guide this study. Data were collected using the 16-item Decisional Conflict Scale, the 9-item Shared Decision-Making Questionnaire, and a 19-item psychological factor list. RESULTS: A total of 304 women participated. Overall, they reported a low-level conflict and high-level involvement. "Cancer not return" was rated as the most important psychological factor influencing mastectomy decisions. Lower decisional conflict was predicted by higher involvement. Higher involvement was predicted by younger age and increased family income. CONCLUSIONS: This study is the first to demonstrate decisional conflict, involvement, and the associated factors specifically in Chinese women undergoing mastectomy. Results determined the importance of several psychological factors influencing mastectomy decisions. Future qualitative studies are required to deepen understanding of women's decision-making experiences regarding this surgery. IMPLICATIONS FOR PRACTICE: Nurses need to provide support to Chinese women in making treatment decisions, especially for older women, and those who are economically disadvantaged. Measures are needed to promote their involvement and improve their understanding of breast cancer and its treatments, which may help reduce decisional conflict, and potentially improve their satisfaction with treatment and quality of life.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".