Patient Experience and Expectations in Oral Health Care: A Nation-Wide Survey
Bibliographic record
Abstract
AIM: This research was conducted to understand patients' dental visit habits and behaviours and to identify factors contributing to a positive patient experience at the dental office. METHODS: An online survey was distributed to members of a market research panel in the United States. Qualifying panellists were 25 years of age or older who visited a dentist in the past year. Survey questions were related to frequency of dental visits, patient satisfaction with the dental office, importance of dental office attributes and dental product recommendation expectations. RESULTS: There were 400 respondents, all from USA, with a mean age of 51 years; 54% were female and 46% were male. Overall, 74% had a college or post-graduate degree. The average number of dental visits per patient per year was 2.1. Fifty percent had been going to the same practice for more than 2 years. 84% of panellists indicated satisfaction with their dental office, which was driven by attributes related to good customer service and quality dental care/services. Factors rated as contributing to patient trust included: offering good services; polite and friendly behaviour; affordable cost; and clear and honest communication. Attributes rated as being most important for a dental practice included: valuing their time; not seeing them as just a patient; gentle dental team; and improving their oral health. Overall, 55% of respondents indicated they expect recommendations for specific brands to treat specific oral care issues. CONCLUSION: Patients are seeking a more personal connection with their dental office and are interested in receiving information about their oral care habits. Including personalised self-care recommendations as part of every dental treatment plan will address these needs and motivate patients to engage in their oral health care. Education, communication and building personal connections are keys to establish a positive patient experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".