“I had already made up my mind.” The impact of prior experience and health care perceptions on decision making in women with early-stage breast cancer.
Bibliographic record
Abstract
228 Background: Shared decision-making (SDM) occurs when informed patients partner with their oncologists to incorporate personal preferences into treatment. Even before engaging with an oncologist about treatment options, patients may have personal experiences or knowledge of other’s experiences with breast cancer that frame their decision-making. This study sought to understand how prior experiences and knowledge drive preferences in early stage breast cancer treatment approaches. Methods: This qualitative study included early stage breast cancer (BC) patients at an academic medical center in the Deep South. Women age ≥18 with an AJCC stage I-III BC diagnosis were invited to complete semi-structured interviews with a trained interviewer. Interviews were audio-recorded, transcribed, and analyzed by two independent coders utilizing a constant comparative method from an a priori conceptual model based on the Ottawa Framework. Major themes and exemplary quotes related to decision-making preferences were extracted. Results: Women (n = 33) interviewed were an average age of 74 (4.2 SD), and 19% of participants were African American. Many women were given the option to omit treatments, such as chemotherapy or radiation therapy, based on hormone receptor status and axillary node involvement. Major themes related to a desire for more treatment were past experiences with family members having cancer or an impression that additional treatment would be more effective. For women that opted out of treatments, prior knowledge of potential physical side effects from friends, family, and other cancer survivors were cited as a major deterrent. Perceptions of low recurrence risk also influenced desire to forgo treatments. Conclusions: Women presenting with early stage BC had varied healthcare experiences, which resulted in preconceived ideas about receiving breast cancer treatments. Consideration of these themes may aid physicians’ ability to address individual concerns to further personalize patient care, thus enhancing the patient-physician partnership. These findings will ultimately assist in improving patient engagement in SDM.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".