Current Situation and Influencing Factors of Vascular Access Decision‐Making Conflicts Among Breast Cancer Chemotherapy Patients in China: A Multicenter Investigation Based on the Ottawa Decision Framework
Bibliographic record
Abstract
Objective To explore the decision‐making conflicts regarding vascular access devices in Chinese breast cancer patients undergoing chemotherapy and the influencing factors. Design A multicenter cross‐sectional survey study. Method A total of 308 breast cancer patients undergoing chemotherapy were included. Questionnaires were conducted using a general information questionnaire, the Chinese version of the Decision Conflict Scale, the Decision Participation Expectation Scale, and the Decision Preparation Scale. Multivariate linear regression analysis was used to investigate the influencing factors of decision conflict regarding vascular access devices in breast cancer patients undergoing chemotherapy. Result The level of decision conflict in the patients was relatively high, with a score of 40.88 ± 9.64, significantly higher than the critical value of 37.5. During the decision‐making process of the patients, 162 cases (52.6%) tended to share the decision with the doctor, but in the actual participation process, 105 cases (34.9%) made the decision passively. There were a negative correlation between patient decision conflict and decision preparation and a positive correlation between patient decision conflict and decision participation. Age, unmarried status, average monthly family income, understanding of the disease, understanding of vascular access, and decision preparation were the main influencing factors of decision conflict. Conclusion There is a discrepancy between the expected decision‐making for vascular access devices among Chinese breast cancer chemotherapy patients and the actual participation. The level of decision conflicts is relatively high, and it is influenced by multiple factors. Medical staff should enhance communication with patients, respect their decision preferences, develop decision support tools, provide appropriate decision support to patients, reduce decision conflicts, and minimize decision regret.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".