Factors Affecting Dental Services Quality in the Private Sector From Patients’ Perspective in Amman-Jordan
Bibliographic record
Abstract
Introduction: The world has been rapidly evolving with a high pace towards clients’ satisfaction. Service providers strive for clients’ satisfaction as it has become more obvious that this is the key to maintain a growing and successful businessObjectives: The objective of this study is to investigate the relationship between the five dimensions of quality of patients’ satisfaction and the quality of dental services.Materials & Methods: Several databases were searched for relevant articles and research papers with an objective revolving around the factors affecting client satisfaction from dental services provided at either a certain clinic, group of clinics, or had a population of a whole city. The articles and research papers covered parts of Eastern Asia, the Middle East, South/North America, and Europe.Multiple regression analysis was performed to study this relationship. The results showed significant relationship between each of the five dimensions of quality with the following weights: Tangibility = 17.7%, Empathy= 17.2%, Responsiveness= 15.6%, Assurance= 14.7% and Reliability= 5.8%. Overall R square = 75.3% adjusted R square is 73.2% with R = 0.868.Conclusion: The unique aspect of this study, which makes it different from other studies, is that it will not only evaluate the dental service received by patients but will also clearly state how a patient defines a good quality dental service according to their priorities to help in reaching of mutual understanding and definition of a good quality service provider.The multiple regression models showed significant relationship between the five dimensions of quality and patients’ satisfaction
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".