Do Patients' Communication Behaviors Provide Insight into Their Preferences for Participation in Decision Making?
Bibliographic record
Abstract
BACKGROUND: The Institute of Medicine report "Crossing the Quality Chasm'' encourages physicians to tailor their approaches to care according to each patient's individual preferences for participation in decision making. How physicians should determine these preferences is unclear. OBJECTIVE: The objective of this study is to assess whether judgments of patient communication behaviors, either globally or individually, can yield insight into patient preferences for participation in decision making. METHODS: Using questionnaire responses to 3 items about the desired level of participation in decision making from a communication study involving 886 audiotaped visits between older patients and surgeons, the authors purposively selected 25 patients who preferred a large role and 25 who preferred a small role in decision making. Two independent raters listened to the audiotapes and coded them for the presence of 7 communication behaviors (question asking, information behavior, initiating, statements of preference, processing, resistance, deference). On the basis of their listening and coding, raters judged patient preferences for participation in decision making. RESULTS: Neither rater accurately judged preferences for participation in decision making beyond chance agreement (kappa statistics: rater 1 = 0.16, rater 2 = 0.20). Inter-rater reliability for the communication behaviors was also generally poor. Area-under-the-curve values for all communication behaviors hovered around 0.50, indicating that none of the behaviors had adequate power to discriminate between patients preferring large versus small roles. CONCLUSION: Patient preferences for participation in decision making cannot be reliably judged during routine visits based on judgments of patient communication behaviors. Engaging patients in a discussion of preferences for decision making may be the best way to determine the role each wants to play in any given decision.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".