A multi-criteria decision-making integrated approach for identifying and ranking factors affecting the quality of cosmetic surgery clinic services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering the increasing demand for cosmetic surgery and the number of private cosmetic surgery clinics, it is essential to measure and manage the quality of services provided by these clinics. Obtaining sufficient knowledge about the content perceived by the clients of the quality of services provided by specialized clinics can affect identifying improvement opportunities and criteria that will cause their competitive advantage, and on the other hand, it also prevents wasting resources. For this purpose, this study aims to identify, evaluate, and prioritize the criteria for quality improvement in cosmetic surgery clinics. First, the effective criteria focus on the quality of medical services have been identified by reviewing the research background. Then, the identified criteria in the case study are customized by the Delphi method, and then the DEMATEL-based analytic network process method (DANP) is applied to reveal their causal relationships between criteria and sub-criteria to determine the direct and indirect influences, and finally, all of them are prioritized. In the end, based on the obtained results and knowledge of experienced medical experts in the case study, some managerial solutions are proposed to improve the quality of the provided medical services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it