“Doctors” or “Influencers”? Physicians’ Presentation of Self in Health Vlogs
Bibliographic record
Abstract
Despite growing interest in doctors’ use of social media, little is known about how medical professionals want to appear before online audiences. This multi-method qualitative study fills this gap by analyzing the self-presentation of 12 Egyptian medical doctors who create health-related video content (vlogs) on social media. We pair in-depth interview and focus groups data with a critical discourse analysis of 48 vlogs to investigate how these physicians construct their images as both health professionals (doctors) and content makers (influencers). In doing so, we rely on Goffman’s dramaturgical approach to examine the “faces” they wear in their vlogs and the strategies they used to manage when and how each face is perceived. We find that participants presented themselves through four faces: Approachable, Knowledgeable, Pedagogical, and Popular. Their self-presentation appears to be a negotiation between two roles: part doctor, or health service provider, and part influencer, or social media content creator.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".