Physicians' Awareness and Attitudes Toward Decision Aids for Patients With Cancer
Bibliographic record
Abstract
PURPOSE: Patient decision aids are interventions designed to help patients make deliberative choices about their treatment options and have been shown to significantly improve patient outcomes. Although considered optimal, decision aids are not widely used in clinical practice for cancer treatment. The objectives of this study are to determine physicians' awareness and use of decision aids, physicians' perceptions of the major barriers to the use of decision aids, and physician characteristics predictive of use of decision aids in clinical practice. METHODS: A population-based survey was mailed to general surgeons, medical oncologists, and radiation oncologists. RESULTS: The survey was mailed to 878 physicians, and the overall response rate to the survey was 64.5%. The majority of the participants were male and working in community hospitals for more than 10 years. Overall, 69% of the respondents were aware of decision aids, and 46% were aware of decision aids relevant to their practice. However, only 24% were currently using decision aids. The main barriers to the use of decision aids were reported as lack of awareness, lack of resources, and lack of time. Multivariate analysis showed specialty to be the only physician characteristic influencing the use of decision aids. CONCLUSION: Approximately one third of physicians treating cancer patients are not aware of what decision aids are, and only 24% are currently using decision aids in clinical practice. Strategies to increase physician awareness about decision aids and to implement these tools into clinical practice are important.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".