Physician Trust in the Patient: Development and Validation of a New Measure
Bibliographic record
Abstract
PURPOSE: Mutual trust is an important aspect of the patient-physician relationship with positive consequences for both parties. Previous measures have been limited to patient trust in the physician. We set out to develop and validate a measure of physician trust in the patient. METHODS: We identified candidate items for the scale by content analysis of a previous qualitative study of patient-physician trust and developed and validated a scale among 61 primary care clinicians (50 physicians and 11 nonphysicians) with respect to 168 patients as part of a community-based study of prescription opioid use for chronic, nonmalignant pain in HIV-positive adults. Polychoric factor structure analysis using the Pratt D matrix was used to reduce the number of items and describe the factor structure. Construct validity was tested by comparing mean clinician trust scores for patients by clinician and patient behaviors expected to be associated with clinician trust using a generalized linear mixed model. RESULTS: The final 12-item scale had high internal reliability (Cronbach α =.93) and a distinct 2-factor pattern with the Pratt matrix D. Construct validity was demonstrated with respect to clinician-reported self-behaviors including toxicology screening (P <.001), and refusal to prescribe opioids (P <.001) and with patient behaviors including reporting opioids lost or stolen (P=.008), taking opioids to get high (P <.001), and selling opioids (P<.001). CONCLUSIONS: If validated in other populations, this measure of physician trust in the patient will be useful in investigating the antecedents and consequences of mutual trust, and the relationship between mutual trust and processes of care, which can help improve the delivery of clinical care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".