A 2-item version of the Japanese Consultation and Relational Empathy measure: a pilot study using secondary analysis of a cross-sectional survey in primary care
Bibliographic record
Abstract
BACKGROUND: The Consultation and Relational Empathy (CARE) measure is a patient-reported measure of physician empathy which is widely used internationally. The Japanese version of the CARE measure has very high internal reliability, suggesting that a shorter version may have adequate validity and reliability. OBJECTIVE: To investigate a valid shorter version of the Japanese CARE measure. METHODS: We conducted a pilot study using secondary analysis of previous data obtained from 9 general practitioners and 252 patients and used to develop the Japanese CARE measure. All 1,023 possible combinations of the Japanese CARE items (n = 1-10) were candidates for the short measure. The internal consistency (Cronbach's alpha) and the correlations between candidate short questionnaires and the original questionnaire were calculated. After selecting the most valid short questionnaire, inter-rater reliability was determined using generalizability theory, and construct validity (Spearman's rho) was determined using patient satisfaction. RESULTS: Two items were selected for a pilot shorter version: item 6 "Showing care and compassion" and item 9 "Helping you to take control." These showed high internal consistency and correlations with the 10-item measure (Cronbach's alpha = 0.920, correlation = 0.979). Forty-five questionnaires per doctor allowed us to reliably differentiate between practitioners. The construct validity for the pilot short measure was high (Spearman's rho 0.706, P < 0.001). CONCLUSION: We generated a pilot 2-item version of the Japanese CARE measure. This pilot 2-item version provides a basis for future validation studies of short CARE measures in other languages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".