Predicting Key Factors Affecting Outpatient Satisfaction in Public Hospitals: Evidence from Erzurum, Turkey
Bibliographic record
Abstract
The main purpose of this paper is to determine background demographic and socio-economic factors affecting outpatient satisfaction of public hospitals. The present study utilized data from a written-questionnaire administered to six hundred and one adult patients who had received outpatient care at various units of three Turkish pubic hospitals. The dependent variable used in predicting student satisfaction was satisfaction levels of the respondents. Due to the ordinal nature of the dependent variable, an ordered logit model was performed to examine demographic and socio-economic determinants of outpatient satisfaction in Erzurum, Turkey. Ordered logit estimation results revealed that type of hospital, marital status, age group, education level, occupation, residential place, monthly income, and information about private hospitals were statistically significant factors of outpatient satisfaction. This paper attempts to present factors affecting outpatient satisfaction in a municipality which was adopted as the leading health care service provider of its region where little work was done. Considering its geographical location, the results of this substantial region may be a valuable contribution for health care managers and policy makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".