تعیین مغایرتهای اجزای الگوی مهارتهای ارتباطی پزشک و بیمار کالگری–کمبریج با نگرش بیماران ایرانی
Bibliographic record
Abstract
Aims: The absence of an appropriate relationship between the patient and the medical staff can lead to verbal and physical arguments and, therefore, lead to intervention from the police and filing a complaint and consequent legal problems. In Iran, patient communication skills are taught to medical students based on the Calgary-Cambridge observational model. As the Iranian society has social, cultural, and belief differences, the present study was designed and performed with the aim of determining the contradictions of the components of this model with the attitude of Iranian patients. Materials and methods: The present cross-sectional study was performed in Imam Khomeini Hospital, Tehran, Iran, in 2016. The sample consisted of conscious patients admitted to the emergency department. A researcher-made questionnaire was used to evaluate patients’ attitudes towards communication skills components based on the Calgary-Cambridge model. The questionnaire consisted of 33 questions regarding various communication skills components, and its reliability and validity were confirmed before being used in this study. Results: Overall, 100 patients with the mean age of 43.1 ± 16.7 years participated in the study, 51% of whom were male. The attitude of patients was contradictory to the guideline in some cases. There was more agreement with shaking hands with the physician among those residing in villages (p = 0.01); men more frequently agreed with being called by their name (p = 0.03), but women preferred to be called “madam” without a name or family name (p = 0.04); 68% of the patients preferred to sit across the physician during the visit; only 31% of the patients believed that the physician should ask for permission before the examination; only 31% of the patients agreed that a physician could look at a patient from the opposite sex. In other items, no significant difference was observed. Conclusion: The attitude of patients participating in the present study was contradictory to the mentioned guideline in some cases. Therefore, it would be better to revise the present model of teaching Calgary-Cambridge communication skills for the physician-patient relationship in some categories according to the culture of the Iranian society.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".