Role of GP empathy on patient-reported outcomes in telephone consultations: a cross-sectional study with validation of the Consultation and Relational Empathy (CARE) Measure
Bibliographic record
Abstract
BACKGROUND: The Consultation and Relational Empathy (CARE) Measure is a validated measure of GP empathy in face-to-face consultations (FTFCs) and higher CARE scores predict better patient outcomes. However, the validity of the CARE Measure and its importance in telephone consultations (TCs) is unknown. AIM: To determine the validity and reliability of the CARE Measure in TCs, and the associations between CARE scores and outcomes in TCs compared with FTFCs. DESIGN AND SETTING: A cross-sectional survey, conducted in 2018, of 1023 patients who had a TC or FTFC with a GP in Scotland within the preceding 30 days. METHOD: Validity and reliability testing of the CARE Measure was conducted using standard methods. Associations between low, medium, and high CARE scores and three patient outcomes (enablement, symptom change, and satisfaction) were determined using multilevel binary logistic regression. RESULTS: Out of the 1023 participants, 369 had TCs and 654 had FTFCs. The CARE Measure was found to be valid and reliable. In TCs, compared with the low CARE score group, a high CARE score was positively associated with enablement (adjusted odds ratio [aOR] 6.4, 95% confidence interval [CI] = 3.5 to 11.9), symptom improvement (aOR 5.7, 95% CI = 2.7 to 11.9), and satisfaction (aOR 20.1, 95% CI = 8.9 to 45.4). Findings were similar in FTFCs,and effects were not influenced by patient or consultation characteristics in either group. CONCLUSION: The CARE Measure is valid and reliable in TC. GP empathy, as measured by the tool, predicts better patient outcomes in TCs, similar to FTFCs. Given the common use of TC in primary care, strategies to support empathic communication are essential irrespective of consultation modality.
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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 | 0.004 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".