A comparison of social media usage and preferences between clear aligner patients and orthodontists
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».