Testing two competitive models of empathic communication in cancer care encounters: A factorial analysis of the CARE measure
Bibliographic record
Abstract
OBJECTIVE: The mechanisms associating physician empathy (PE) with patient outcomes remain unclear. PE can be considered as a whole (one process) or three subcomponents can be identified (an establishing rapport process; an emotional process; a cognitive process). The objective was to test two competitive models of PE in cancer care: a three-process model adapted from Neumann's model versus a one-process model, with the use of the Consultation and Relational Empathy measure (CARE). METHODS: The CARE was completed by 488 oesogastric cancer patients from the national French database FREGAT. A confirmatory factor analysis (CFA) and a bifactor model were performed to test the two competitive models. RESULTS: The CFA revealed that the one-factor structure showed a moderate fit to the data whereas the three-factor structure showed a good fit. However, the bifactor model favoured unidimensionality. CONCLUSION: We cannot provide a clear-cut conclusion about whether PE should be considered as on unique process or not. Further work is still needed. Meanwhile, one should not preclude the use of three subscores in cancer care if specific elements of the encounter need to be assessed.
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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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".