If Research is Not the Evidence, What is it? Egyptian Physicians’ Explanations of the Lack of Research Citations in their Health Vlogs
Bibliographic record
Abstract
Abstract Doctor-influencers have the knowledge needed to understand scientific research, and they have the social media popularity to share it with large audiences. In this article, I explore the treatment of evidence in vlogs by Egyptian doctor-influencers: how they present it and what they use instead of it. I answer the following questions: 1) How do doctor-influencers present evidence on social media? 2) If evidence is not the research, then what is it? The data was collected through in-depth interviews with 12 Egyptian doctor-influencers from different specializations; two focus groups; and a critical discourse analysis of 48 of the influencers’ most popular and most engaging videos. I found that the doctor-influencers cited academic research only in the videos about medical controversies, new treatments, misinformation, or common medical mistakes. In most of their videos, the Egyptian doctor-influencers scarcely referred to medical research. This is because they believe that their audiences would not understand evidence due to their low educational attainment, weak research skills, and the language barrier (as most medical research is published in English rather than Arabic). These findings highlight the challenges of communicating science in societies with a low level of education. In these settings, communicators may need to move beyond mediating research studies on social media and instead focus on simplifying, translating, and connecting evidence-based information to the cultural context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".