Factors Influencing Customer Retention and Loyalty in Dental Practice in the United States
Bibliographic record
Abstract
This mixed study is an analysis of factors that influence customer retention and loyalty in dental practices in the US. The study determines and encourages patient choice to visit specific dental offices, ensures the well-being of dental customers, and supports the viability of dental practices in the industry. A mixed research methodology was employed, which started with a thematic content analysis of the sampled literature. This produced 18 influencing factors for the dental industry. Afterward, these factors were constructed into survey questions to conduct the primary research via SurveyMonkey and identify dental patient perceptions regarding these 18 factors. Finally, a non-parametric Kruskal-Wallis test was employed to rank and group the 18 factors based on their importance as perceived by US dental patients. The study highlighted the similarities and differences in the influencing factors across extant literature and contemporarily collected data in the US. While the extant literature ranked communication and relation as essential factors in retaining dental patients, the survey prioritized skill and trust as the crucial factors a patient would consider when choosing a dental clinic. This study grouped and ranked all 18 of the identified factors, which any dental clinic could consider retaining and enhancing the loyalty of their existing and prospective clients.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".