Preferences of Iranians to select the emergency department physician at the time of service delivery
Bibliographic record
Abstract
BACKGROUND: Understanding patient preferences in emergency departments (EDs) can provide useful information to enhance patient-centred care and improve patient's experience in hospitals. This study sought to find evidence about patients' preference for physicians when receiving services in EDs in Iran. METHODS: In this discrete choice experiment survey, 811 respondents completed the scenarios with 5 attributes, including type of physicians, price of services, time to receive services, physician work experience, and physician responsibility. Analyses were conducted for different social and economic groups as well as for the total population. RESULTS: This study showed that the willingness to pay (WTP) for being visited by a physician with a high sense of responsibility was 67.104US$. WTP for being visited by an emergency medicine specialist (EMS) was 22.148US$. WTP for receiving ED services 1 min earlier was 0.417US$ and for being visited by 1 year higher experienced physician was 0.866US$. WTP varied across different age groups, sex, health status, education, and income groups. CONCLUSION: As the expertise and experience of providers are important factors in selecting physicians in EDs by the patients, providing this information to patients when they want to select their providers can promote patient-centred care. This information can decrease patients' uncertainty in the selection of their services and improve their experience in hospitals.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".