A Comparison Study of Communication Skills between General Surgery and General Practice Residents on First-time Patient Visits
Bibliographic record
Abstract
Background: There is little published research about differences in doctor-patient communication of different specialties. Accordingly, we compared doctor-patient communication skills in two different specialties, general surgery (GS) and general practice (GP). Methods: Twenty residents training at the Bahrain Defence Force Hospital (10 men and 10 women; mean age 28 years; 10 GS and 10 GP) participated in 200 patient first visit consultations. The consultations were video-recorded and analysed by four trained observers using the MAAS Global scale. Results: 1) Internal consistency reliability of the MAAS Global (> 0.91) and Ep2 = 0.84 for raters was high, 2) GP residents spent more time (12 minutes) than GS residents (7 minutes), in the visits, 3) There were several differences on the MAAS Global items between GP and GS residents (GS > GP, p < 0.05 on history taking, diagnosis and medical aspects; GP > GS, p < 0.05 on information giving), and 4) The present participants performed well compared to normative samples as well as to criterion-referenced cut-off scores. The general level of communication skills in both specialties, however, was ‘unsatisfactory’ and ‘doubtful’, as it is for normative samples. Conclusion: Excellent doctor-patient communication is essential but does not appear to receive the amount of attention that it deserves in practice settings. There are some differences between specialties as well as unsatisfactory communication skills for both specialties, since residents from both programs spent less time than recommended on each consultation. Our findings emphasize the need to improve the communication skills of physicians in general and for surgeons in particular.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".