Assessment of Doctor-Patient Communication Among Residents in Internal Medicine Polyclinic At RSUP Dr. Mohammad Hoesin Palembang 2014 Using Simplified Checklist of Calgary Cambridge Guide
Bibliographic record
Abstract
Introduction : Misperceptions between doctors and patients can bring negative impact for both the doctors and patients. Misperceptions may occur due to miscommunication during doctor-patient communication. Therefore, assessment during the communication process is necessary. Methods : This study was a descpritive study with qualitative approach. Checklist Calgary Cambridge Guide (CCCG) was chosen as instrument because it has been widely used in many country.The study was condicted in Polyclinic Internal Medicine of dr. Mohammad Hoesin Hospital due to its high patient load with various diseases that is suitable for doctor-patient communication observation. Subjects were six residents in the department. Observation was done during the communication process. Deep interview was then done to assess the resident’s knowledge and opinions in doctor-patient communication and barriers related to it. Results : Majority of the residents failed to do some points of the CCCG, which includes self introduction, role and nature of interview, obtain consent and explain process, obtain permission prior to physical examination. Conclusions : In conclusion, the doctor-patient communication among residents in Internal Medicine Polyclinic At RSUP Dr. Mohammad Hoesin Palembang.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".