Does location matter? A study of the public’s preferences for surgical care provision
Bibliographic record
Abstract
OBJECTIVES: As in other countries, office-based surgeons, outpatient clinics and hospitals in Germany are in increasing competition to each other. However, little is known about potential patients' preferences for surgical care provision. The aim of this study was to investigate the general public's preferences towards location and other attributes of surgical treatment. METHODS: An economic evaluation technique, discrete choice analysis, was administered in a survey to a sample of a German general public Internet panel, representative in terms of age, gender and education. Responders were asked to choose their preferred provider of surgical care in a series of pairwise choices, defined by five attributes. Regression analysis was used to quantify preferences towards characteristics of care and to calculate utilities of treatment scenarios. The strength of preferences and the rate at which participants were willing to trade among attributes were estimated. RESULTS: The response rate was 76% (n=1134). 'Specialization and experience of provider' was the single most important attribute, followed by 'waiting times' and 'staff continuity'. Subjects traded a 4-week waiting time to obtain surgery at a highly specialized institution. Responders favoured outpatient clinics over office-based and inpatient surgery, but 'location of care' preferences were only weak. Whether participants had undergone surgery in the past had only minor effect on their preferences. CONCLUSIONS: Potential patients base their choice between providers mainly on characteristics of care delivery and not location of care. The competition between and among providers of surgical care of different types of institutional organization will be determined by what is offered to patients.
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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 | 0.005 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".