Decisional Conflict in Patients and Their Physicians: A Dyadic Approach to Shared Decision Making
Bibliographic record
Abstract
BACKGROUND: Decisional conflict is defined as personal uncertainty about which course of action to take when choice among competing options involves risk, regret, or challenge to personal life values. It is influenced by inadequate knowledge, unclear values, inadequate support, and the perception that an ineffective decision has been made. Until recently, it has been studied at the individual level, which ignores the interpersonal system between patients and physicians. OBJECTIVE: To explore the effect of feeling uninformed, unclear values, inadequate support, and the perception that an ineffective decision has been made on one own's outcome (actor effect) and on the other person's outcome (partner effect). METHODS: After a clinical encounter, modifiable deficits and personal uncertainty were measured in physicians and patients using the Decisional Conflict Scale. Structural equation modeling was used to measure the parameters of the Actor-Partner Interdependence Model. RESULTS: A total of 112 dyads of physicians and patients were included in the analysis. For both patients and physicians, 2 actor effects, unclear values (P < 0:0001) and the perception that an ineffective decision has been made (P < 0:0001), were found to be positively correlated with personal uncertainty. One partner effect, feeling uninformed (P=0:03), was found to be negatively correlated with personal uncertainty. CONCLUSIONS: Personal uncertainty of patients and physicians is influenced not only by their respective deficits but also by the deficits of the other member of the dyad. Our results indicate that the more unclear the expression of their own values and the more they perceive that an ineffective choice had been made, the more both physicians and patients experience personal uncertainty. They also indicate that the less uninformed they feel, the more both physicians and patients experience personal uncertainty.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".