A Controlled Study of Salon-training Models Based on the Calgary-Cambridge Guidelines for Improving Patient-physician Communication Skills in General Practice
Bibliographic record
Abstract
Background Doctor-patient communication is one of the core abilities of general practitioners. Dophisticated doctor-patient communication skills are the basis for building a harmonious doctor-patient relationship, and help to improve patients' sense of gain and satisfaction in seeking medical treatment. However, the communication ability of general practitioners in China is generally low, it is necessary to explore a training mode of doctor-patient communication that adapts to China's national conditions and meets the communication needs of general practitioners in China to improve the communication ability between doctors and patients. Objective To explore the application effect of salon training based on the Calgary-Cambridge Guide in the training of doctor-patient communication ability of resident doctors in standardized training of general practitioners, and to provide reference for the construction of doctor-patient communication training system. Methods Forty cases of general practitioners in Chengdu Fifth People's Hospital Standardized general practice training from 2019 to 2020 were selected as the research object, and randomly divided into salon group and control group, with 20 cases in each group. Salon group used salon training mode based on Calgary-Cambridge guidelines for doctor-patient communication training, while the control group was set as blank control. Before the training and one week after the training, the standardized patient (SP) model was adopted to clinical reception in the two groups, and the doctor-patient communication evaluation scale (SEGUE) was used to evaluate the level of doctor-patient communication, and the training effects of the two groups were compared. Results Finally, 28 cases were included, including 15 cases in the salon group and 13 cases in the control group. After the training, the score of the SEGUE scale of the training doctors in the salon group increased from (11.80±4.36) to (18.07±4.11), and that of the training doctors in the control group increased from (12.15±4.63) to (14.46±3.71). The score of SEGUE scale in Sharon group after training was significantly different from that before training (t=3.250, P< 0.001). There was no significant difference in the score of SEGUE scale between the control group after training and before training (t=2.582, P=0.624). After training, 25 items in the SEGUE scale were analyzed, and the difference between the salon group and the control group was statistically significant (P<0.05). The results of the following five items in the salon group were better than those in the control group: "Establishing personal trust relationship" (93.3% vs. 7.7%) and "Protecting patients' privacy/respecting patients' right to choose" (53.3% vs. 15.4%) in the preparation stage; Understand the patient's stage of "recognizing the patient's efforts, changes and difficulties" (33.3% vs. 23.1%) and "expressing concern and making the patient feel warm/confident" (100.0% vs. 69.2%) ; At the end of the consultation stage, "Ask the patient if there are any other questions to discuss" (66.7% vs. 23.1%) . Conclusion Salon training mode based on Calgary-Cambridge guidelines can enhance students' interest and enthusiasm in participating in the training, which has a good training effect on improving the communication ability between doctors and patients, and is worth learning and popularizing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".