Modification of Calgary Cambridge for Indonesian medical students: Communication guidelines
Bibliographic record
Abstract
Effective communication skill applied by doctors in the doctor-patient consultation process becomes one of important factors that can improve the consultation outcomes such as patient satisfaction, adherence to treatment and recovery process. However, effective doctor-patient communication is rarely applied in practice. Limited consultation time, patient overload, doctor burnout, and poor communication skills are among the factors that cause ineffective doctor-patient communication process. This study aimed to develop a new effective communication guideline for doctor-patient communication in Indonesia by modifying the Calgary Cambridge medical interview guide. This study uses qualitative approach within four stages: expert panel, student panel and focus group discussion, expert review, and trials. Informants were chosen purposively. Three points of high category, 24 points of middle category and 44 points of low category are resulted from expert panel stage which consists of specialist representatives from 12 clinical divisions in Dr. Moehammad Hoesin central public hospital (RSMH), Palembang, South Sumatera, Indonesia. The high and middle category were discussed by two groups of internships doctors in RSMH in student panel and focus group discussion (FGD) session. The results were validated by a doctor-patient communication expert (expert reviewer) and then tested by the internship doctors through role play at the trial stage. The final result yields eight main points and eleven effective tips of the Calgary Cambridge Guide checklist modification with five to six minutes effective consultation time. This modified guideline is appropriately applicable for doctor-patient communication in daily consultation in Indonesian practical, social and cultural context.
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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".