Understanding Medical Decision-making in Prostate Cancer Care
Bibliographic record
Abstract
The availability of several treatment options for prostate cancer creates a situation where patients may need to come to a shared decision with their health-care team regarding their care. Shared decision-making (SDM) is the concept of a patient and a health-care professional collaborating to make decisions about the patient's treatment course. Nurse navigators (NNs) are health-care professionals often involved in the SDM process. The current project sought to evaluate the way in which patients with prostate cancer make decisions regarding their care and to determine patients' perspectives of the role of the NN in the SDM process. Eleven participants were recruited from the Prostate Assessment Centre by a NN. They were interviewed via telephone and their responses were analyzed using thematic analysis. Five interacting factors were determined to influence the way participants made decisions including level of anxiety, desire to maintain normalcy, support system quality, exposure to cancer narratives, and extent of practical concerns. NNs were found to increase knowledge, decrease indecision, and provide reassurance for participants. Based on the beneficial aspects of NN interaction reported in this study, the use of NNs in SDM programs should be encouraged. The results of the study demonstrate the complexity of the decision-making process when it comes to prostate cancer treatment. The factors elucidated in the study should be considered during the development and implementation of prostate cancer SDM programs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".