Abstract: Plastic Surgery-Related Hashtag Utilization on Instagram and Implications for Education and Marketing
Notice bibliographique
Résumé
INTRODUCTION: Recent data suggests 42 percent of surgeons report their patients are seeking aesthetic surgery to improve their appearance on Instagram and other social media.1 Despite the rising influence of Instagram in plastic surgery, few academic publications address Instagram, let alone evaluate its utilization in plastic surgery. We thus set out to systematically answer the following two questions: 1) what plastic surgery-related content is being posted to Instagram, and 2) who is posting this content? METHODS: Twenty-one Instagram plastic surgery related hashtags were queried. Content analysis was used to qualitatively evaluate each of the nine “top” posts associated with each hashtag (189 posts). Duplicate posts and those not relevant to plastic surgery were excluded. RESULTS: A total of 1,789,270 posts utilized the twenty-one hashtags sampled in this study. Of the top 189 posts for these 21 queried hashtags, 163 posts met inclusion criteria. American Board of Plastic Surgery (ABPS) and Royal College of Physicians and Surgeons of Canada (RCPSC) board certified plastic surgeons accounted for only 17.8% of top posts (29 posts), whereas those not board certified by ABPS or RCPSC accounted for 26.4% (43 posts). Excluding foreign surgeons, otolaryngologists made up the largest group of non-ABPS or RCPSC board certified surgeons, with 7.4% of top posts (12 posts). Also included in this cohort were dermatologists (9 posts), general surgeons (6 posts), gynecologists (4 posts), family medicine physicians (2 posts), and an emergency medicine physician (1 post). All of these non-plastic surgery trained physicians marketed themselves as “cosmetic surgeons”. Nine of these top posts (5.5%) were by non-physicians. This included dentists (4 posts), spas with no associated physician (4 posts), and a hair salon (one post). The majority of these posts were for self-promotional (94 posts, 67.1%) as opposed to educational (46 posts, 32.9%) purposes. Board certified plastic surgeons were significantly more likely to post educational content to Instagram as compared to non-plastic surgeons (62.1% vs. 38.1%, p = .02). CONCLUSION: ASPS board eligible and board-certified plastic surgeons are underrepresented amongst physicians posting top plastic surgery-related content to Instagram. Given that the increasing number of non-plastic surgeons performing cosmetic procedures may come at the expense of patient safety and outcomes,2–4 our findings as mentioned here present a possible cause for concern. Reference Citations: 1. American Academy of Facial Plastic and Reconstructive Surgery Annual Survey Statistics. 2017 Jan. Available at: http://www.aafprs.org/media/stats_polls/m_stats.html. 2. Mioton LM, Buck DW II, Gart MS, Hanwright PJ, Wang E, Kim JY. “A Multivariate regression analysis of panniculectomy outcomes: Does plastic surgery training matter?” Plast Reconstr Surg. 2013; 131: 604e-612e. 3. O’Donnell J. “Lack of training can be deadly in cosmetic surgery.” USA Today. September 15, 2011. 4. Shah A, Patel A, Smetona J, Rohrich RJ, “Public Perception of Cosmetic Surgeons versus Plastic Surgeons: Increasing Transparency to Educate Patients.” Plast Reconstr Surg. 2017 Feb; 129(2):544e-557e.
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 | 0,003 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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 ».