What Makes Patients Stick with an Orthopedic Surgeon?
Bibliographic record
Abstract
Objectives Patient loyalty is a determinant of continued care, adherence to the provider’s recommendations, and patient compliance which affects the overall health care. This study aimed to assess the determinants of patient loyalty to an orthopedic surgeon. Methods This cross-sectional study was performed on 190 patients in an academic orthopedic clinic. The checklist included 14 items grouped into three categories scoring from 1 (unimportant) to 5 (very important), including cheerful face, tone of speech, follow-up, truthfulness, empathy, gender, age, attire, attentive posture, skill and expertise, number of publications, academic activity, the title of certification (MD, Ph.D), and position (e.g., chief of service, dean of the department). Each item was scored separately for “staying with a physician” and “recommending to others.” Other variables collected were age, education, condition, and type of visits (new patient, follow-up, and postop). Results Providers’ physical characteristics (gender, age, attire, and attentive posture) and academic achievements (position, publication, and degree) scored low to moderate, between 2 and 3 out of 5. The ‘skill and expertise’ item scored the highest, followed by all behavioral aspects, including cheerful face, tone of speech, follow-up, truthfulness, and empathy. There was no significant difference between “staying with the same physician” and “recommending to others.” The item scores showed no significant difference between males and females, occupation, education, and the type of visit. Conclusion Providers’ attitudes and expertise are the most important determinants correlated with patient loyalty indicating the critical role of the provider’s behavior in patient adherence and being recommended to friends and family. Of note, the physical characteristics of the provider showed little role in sticking with the same provider for continued care. Although skill and expertise might correlate with scheduling the first visit, still attitude and behavioral factors may be correlated with sticking with the same provider for continued care.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".