The impact of physicians' reactions to uncertainty on patients' decision satisfaction
Bibliographic record
Abstract
RATIONALE: Patients' and physicians' response to uncertainty may affect decision outcomes. The purpose of this study was to explore the impact of patients' and physicians' reactions to uncertainty on patients' satisfaction with breast health decisions. METHODS: Seventy-five women facing breast cancer prevention or treatment decisions and five surgeons were recruited from a breast health centre. Patients' and physicians' anxiety from uncertainty was assessed using the Physicians' Reactions to Uncertainty Scale; wording was slightly modified for patients to ensure the scale was applicable. Patients' decision satisfaction was assessed 1-2 weeks after their appointment. A mixed-effects logistic regression model was used to assess associations between patients' and providers' anxiety from uncertainty and patients' decision satisfaction. A provider-specific random effects term was included in the model to account for correlation among patients treated by the same provider. RESULTS: Patients' decision satisfaction was associated with physicians' anxiety from uncertainty (beta = 0.92, P < 0.01), but not with patients' anxiety from uncertainty (beta = -0.18, P > 0.27). CONCLUSIONS: This study suggests that physicians' reactions to uncertainty may have an effect on decision satisfaction in patients. More research is needed to confirm this relationship and to determine how to help patient-provider dyads to manage the uncertainty that is inherent in most cancer decisions.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".