Patient Trust
Bibliographic record
Abstract
BACKGROUND: Patients' trust in their health care providers may affect their satisfaction and health outcomes. Despite the potential importance of trust, there are few studies of its correlates using objective measures of physician behavior during encounters with patients. METHODS: We assessed physician behavior and length of visit using audio tapes of encounters of 2 unannounced standardized patients (SPs) with 100 community-based primary care physicians participating in a large managed care organization. Physician behavior was assessed via 3 components of the Measure of Patient-Centered Communication (MPCC) scale. The Primary Care Assessment Survey (PCAS) trust subscale was administered to 50 patients from each physician's practice and to SPs. We used multilevel modeling to examine the associations between physicians' Patient-Centered Communication during the SP visits and ratings of trust by both patients and SPs. RESULTS: Component 1 of the MPCC, which explored the patient's experience of the disease and illness, was independently associated with patient's rating of trust in their physician. A I SD increase in this score was associated with 0.08 SD increase in trust (95% confidence interval 0.02-0.14). Each additional minute spent in SP visits was also independently associated with 0.01 SD increase in patient trust. (95% confidence interval 0.0001-0.02). Component 1 and visit length were also positively associated with SP trust ratings. CONCLUSIONS: Physician verbal behavior during an SP encounter is associated with trust reported by SPs and patients. Research is needed to determine whether interventions designed to enhance physicians' exploration patients' experiences of disease and illness improves trust. Key Words: physician-patient relationship, patient-centered care, trust, physician behavior
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".