Doctor-Patient Communication skills: Knowledge and Practice among Physicians of private medical colleges of Chattogram, Bangladesh
Bibliographic record
Abstract
Background: Quality patient care and fostering doctor- patient relationships with compassion and mutual respect rely on effective communication. This study sought to evaluate the physicians' knowledge and proficiency in doctor-patient communication skills across three private medical colleges and hospitals in Chattogram, Bangladesh. Materials and Methods: From April to May 2024, Chattogram, Bangladesh's Southern Medical College (SMC), BGC Trust Medical College (BGC TMC), and Chattogram Maa O Shishu Hospital Medical College (CMOSHMC) conducted a descriptive cross-sectionalresearch. A total of 384 physicians voluntarily participated in this study. A structured questionnaire including Knowledge and Practice towards Doctor-Patient Communication skills based on Calgary- Cambridge framework was distributed among the physicians. Data were analyzed by Statistical Package of Social Sciences version 24. Results: In this study, out of 384 physicians, 168(43.8%) were male and 216 (56.3%) were female. The mean age of the physicians was 33.0859 ± 9.241. 142(37%) were from Chattogram Maa O Shishu Hospital Medical College (CMOSHMC), 132(34.4) from BGC Trust Medical college (BGCTMC) and 110(28.6) from Southern Medical College (SMC). The mean of total knowledge score was 41.914 (SD: ±3.893) and total practice score was 38.554 (SD: ±4.317). According to Bloom's scale, the doctors in this research had a moderate degree of practice (76%) but an excellent level of knowledge (>80%) about doctor-patient communication. Conclusion: Though physicians in this study had a good level of knowledge but illustrated a Moderate level of Practice on Doctor Patient Communication skill. The barriers to practice good communication skills must be overcome for a better Doctor Patient Relationship.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".