Social Media Influence on Surgeon Selection Among Iranian Maxillofacial Patients: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Social media has reshaped health care decision-making; however, its influence on maxillofacial surgeon selection in non-Western contexts such as Iran remains underexplored. Understanding how patients balance digital platforms (eg, Google, Instagram) with traditional referral networks can inform trust dynamics and patient-centered care strategies. Objective: This study aimed to evaluate the impact of social media compared to personal recommendations on maxillofacial surgeon selection among Iranian patients, assessing decision-making factors, trust perceptions, accuracy concerns, and demographic influences. Methods: A cross-sectional survey of 384 patients at maxillofacial surgery clinics in Isfahan, Iran (September-November 2023), was conducted using structured questionnaires to collect data on demographics, surgeon selection pathways, social media use, trust, and accuracy concerns. Descriptive statistics, χ2 tests, one-sample t tests, and multiple linear regression were conducted using SPSS Version 26 to analyze platform impact and predictive variables. Results: Personal recommendations dominated surgeon selection (239/384, 62.2%), significantly outweighing Google (75/384, 19.5%) and Instagram (11/384, 2.9%; χ²=214.3, P<.001). Google and Instagram were used by 160 (41.7%) and 119 (31.0%) patients, respectively; however, their decision-making impact was low with (mean scores: Google 2.27 (0.82), Instagram 2.14 (SD 0.79) on a 1-5 scale; t tests: P<.001). Patient-generated content drove trust, with reviews valued by 144 (37.5%) for Google and 157 (40.9%) for Instagram, and testimonials by 174 (45.3%) for Instagram. Professional credentials influenced 116 (30.2%) participants for Google. Accuracy concerns were moderate; (means values of Google 2.84 (SD 0.91), Instagram 2.85 (SD) 0.88; P<.05). Regression identified recommendations (β=.42, P<.001), credential trust (β=.19, P=.002), and review authenticity (β=.14, P=.02) as predictors, while social media use was not a significant predictor (P=.32). Participants were predominantly female (233/384, 60.7%), aged 21-30 years (117/384, 30.5%), employed (159/384, 41.4%), with moderate income (201/384, 52.3%), and no prior surgery (205/384, 53.4%). Instagram use was higher among younger patients (21-30 years: 48/117, 41.0%; χ²=12.4, P=.006). Conclusions: Social media plays a supplementary role in the selection of maxillofacial surgeons in Iran, with traditional networks prevailing due to cultural trust and low health literacy (adequacy in 43% patients). The emphasis on credible reviews and credentials underscores the need for verified digital content. Contrasting with the digital reliance on aesthetic surgery, these findings advocate for verified profiles, patient education portals, and culturally tailored strategies to enhance trust and patient-centered care.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".