The Trend of Four Consultation Models in Four Specialties at Four Tertiary Care Hospitals
Bibliographic record
Abstract
Background and Aim: Multiple consultation models exist in medical practice. A comprehensive doctor-patient relationship serves as a foundation to bring about a positive outcome in terms of patient health. However, evidence of doctors’ impact on improving patients’ mental and physical health through a specific model is sparse. This study aimed to identify the most common consultation models adopted in four different specialties at four hospitals in Riyadh, Saudi Arabia. Methods: From four tertiary care hospitals of Riyadh, clinicians (n=263) with clinical experience >3 years from Internal Medicine, Surgery, Family Medicine, and Psychiatry departments participated in this observational study. A 27-items questionnaire describing five consultation models was carried out in hard copy and a soft copy using the Snowball sampling method to receive the responses that were analyzed by using SPSS version 23.0 in the form of descriptive results. Results: Out of 263, most clinicians (n=121, 46.0 %) were found to practice a blended consultation approach while dealing with patients. The 2nd most common adopted consultation model was the Deliberative model (n= 109, 41.4%). Other consultation (Informative and Interpretive) models were the least practiced models (1.5%). Conclusion: The blended consultation is found to be the most practiced consultation model. A clinician should adopt an attitude that is flexible and empathetic towards patients’ needs and expectations. Consideration should be given to assisting physicians in adapting their roles for interpersonal styles to the preferences of various patients. This expanded role will result in improved health outcomes for diverse populations utilizing health care. Key words: Deliberative model, Doctor Behavior, Family Medicine, Interpretive Model, Paternalistic Model, Informative Model, Psychiatry
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".