The “Preparation for Shared Decision-Making” Tool for Women With Advanced Breast Cancer: Qualitative Validation Study
Bibliographic record
Abstract
BACKGROUND: The range of decisions and considerations that women with advanced breast cancer (ABC) face can be overwhelming and difficult to manage. Research shows that most patients prefer a shared decision-making (SDM) approach as it provides them with the opportunity to be actively involved in their treatment decisions. The current engagement of these patients in their clinical decisions is suboptimal. Moreover, implementing SDM into routine clinical care can be challenging as patients may not always feel adequately prepared or may not expect to be involved in the decision-making process. OBJECTIVE: Avalere Health developed the Preparation for Shared Decision-Making (PFSDM) tool to help patients with ABC feel prepared to communicate with their clinicians and engage in decision making aligned with their preferences. The goal of this study was to validate the tool for its acceptability and usability among this patient population. METHODS: We interviewed a diverse group of women with ABC (N=30). Interviews were audiorecorded, transcribed, and double coded by using NVivo. We assessed 8 themes to understand the acceptability and usability of the tool. RESULTS: Interviewees expressed that the tool was acceptable for preparing patients for decision making and would be useful for helping patients know what to expect in their care journey. Interviewees also provided useful comments to improve the tool. CONCLUSIONS: This validation study confirms the acceptability and usability of the PFSDM tool for women with ABC. Future research should assess the feasibility of the tool's implementation in the clinical workflow and its impact on patient outcomes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".