A comparison of social media usage and preferences between clear aligner patients and orthodontists
Bibliographic record
Abstract
Purpose: The purpose of the current study was three-fold: (1) to evaluate the social media use and preferences of prospective clear aligner patients; (2) to investigate whether differences in social media use exists between patients seeking clear aligner therapy and orthodontists that provide clear aligner therapy; and (3) to explore the preferences of clear aligner patients for their treatment provider. Methods: An online survey was administered by Dynata (Shelton, CT) to prospective patients in Canada and the United States. Questions were designed to describe patient demographics and social media use, preferences, and content interest. Archival data was used to describe orthodontist demographics and online marketing strategies. ANOVAs with corresponding Dunnett’s t-tests and Pearson’s’s chi-squared were used to analyze parametric and non-parametric data, respectively. Results: 204 patients and 211 orthodontists completed the two surveys. Patients preferred an orthodontist as the treatment provider (x ̅=4.11), followed by their family dentist (x ̅=4.02). The three most common social media platforms used by patients are YouTube, Facebook, and Instagram (x ̅=4.47, 4.41, 4.05). Gender differences in social media usage revealed that female patients have higher engagement on LinkedIn, YouTube, and Twitter (p<.0001, p<.05, p<.0001) compared to males, particularly on Tuesdays, Thursdays, Fridays, and Saturdays, and during evenings (p<.05, p<.05, p<.01, p<.01, p<.05). USA patients showed increased Twitter usage (p<.05), particularly in the early mornings (p<.05) and expressed higher interest in viewing patient photos/videos (p<.01), compared to Canadian patients. Patients aged 19-29 are more active on social media during nighttime compared to patients aged 40-65 (p<.05) and exhibit higher Facebook usage compared to those aged 30 and above (p<.05). However, they engage significantly less on TikTok (p<.05). Employment status impacts social media preferences, with full-time employed patients preferring orthodontists (p<.0001) and showing greater interest in contests and hashtags (p<.05, p<.05). Patients surpass orthodontists in social media activity across all platforms (p<.0001), with peak activity on Monday, Friday, and Saturday evenings (p<.0001). Content related to treatment costs is crucial for patients, and before/after photos are the most interesting (p<.0001). Notable variations exist between patients and orthodontists regarding the days, times, and content focus on social media (p<.0001, p<.01, p<.0001), as well as the significance of interactions like hashtags, polls, live sessions, and sharing patient posts (p<.0001, p<.0001, p<.0001). Conclusions: Patients predominantly use Facebook daily, with YouTube being the second most popular platform. Likewise, Facebook is the preferred platform among orthodontists. However, patients show significantly higher activity across all platforms compared to orthodontists, including LinkedIn, Facebook, Instagram, TikTok, Snapchat, YouTube, Twitter, and Pinterest. Patients’ engagement is particularly notable during the evening hours at the beginning and end of the week, as well as on weekends. In contrast, orthodontists exhibit increased activity throughout the week, particularly during morning and afternoon hours, corresponding to regular working days. To better align with patient behaviours, orthodontists may consider adjusting their social media marketing strategies.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".