Exploring the relationship between practice demographics, online presence, and social media use for clear aligner therapy: a survey of general dentists and orthodontists
Bibliographic record
Abstract
Purpose: The purpose of the present study was three-fold: (1) to investigate the effect of practice/practitioner demographics on social media marketing by general practitioners (GPs) and orthodontists (Orthos) who provide clear aligner therapy; (2) to evaluate whether any differences in online marketing strategies exist between both groups; and (3) to investigate whether there have been any changes in social media marketing since the onset of COVID-19. Methods: An electronic survey was distributed to GPs and Orthos in Canada and the United States. Questions were designed to describe practice/practitioner demographics and online marketing strategies. Pearson’s chi-squared, Mann Whitney, and Spearman correlation tests were used to analyze the data. Results: 86 (29.0%) and 211 (71.0%) GPs and Orthos completed the survey. Orthos were significantly more likely to incorporate social media (p=0.002), practice websites (p<0.001), teledentistry (p<0.001), and online reviews (p=0.003) compared with GPs. Although Facebook and Instagram were the most frequently used platforms among both groups, Orthos reported significantly more frequent use of Facebook (p=0.002), Instagram (p<0.001), TikTok (p=0.001), Snapchat (p=0.048) and YouTube (p=0.028). More experienced practitioners were significantly more likely to use LinkedIn (rs=.243, p<0.01), Snapchat (rs=.249, p<0.01), YouTube (rs=.179, p<0.01), Twitter (rs=.211, p<0.01), and Pinterest (rs=.146, p<0.05). Those practicing in more heavily populated regions were significantly more likely to use Instagram (rs=.155, p<0.05) and Twitter (rs=.137, p<0.05). More frequent use of Instagram, TikTok, Facebook, YouTube, and Snapchat was significantly positively correlated with more new patients starts. There was no statistically significant difference in self-reported social media marketing since the onset of COVID-19. Conclusions: The most popular online marketing tools included practice websites, social media networking (particularly Facebook and Instagram), and online reviews. Orthos were more likely to use these strategies to market their practice. Over the last five years, GPs and Orthos have increased their social media marketing footprint, and this trend is expected to continue to increase in the future. Practitioners who use teledentistry, social media (e.g., Instagram, TikTok, Facebook, YouTube, and Snapchat), practice websites, online reviews, and search engine optimization reported more annual new patients starts, suggesting that these tools may be valuable adjuncts to existing marketing practices.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".