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 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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".