Pattern hair loss and health care professionals: How well are we connecting with our audience?
Bibliographic record
Abstract
BACKGROUND: Pattern hair loss, the most common form of hair loss, affects millions in the United States. Americans are increasingly seeking health information from social media. It would appear that healthcare professionals contribute relatively minimally to pattern hair loss content, thereby posing serious concerns for credibility and quality of information available to the general public. OBJECTIVES: This study evaluates popular pattern hair loss-related content on Instagram, TikTok, and YouTube, aiming to understand effective engagement strategies for healthcare professionals on social media. METHODS: The top 60 short-form videos were extracted from Instagram, TikTok, and YouTube, using the search term "pattern hair loss" and inclusion of USA-based accounts only. Videos were categorized by creator type (healthcare vs. non-healthcare professional), content type (informational, interactional, and transactional), and analyzed for user engagement and quality, using engagement ratios and DISCERN scores, respectively. CONCLUSIONS: Healthcare professionals, especially dermatologists, play a crucial role in delivering credible information on social media, supported by higher DISCERN scores. Multi-platform presence, frequent activity, and strategic content creation contributes to increased reach and engagement. Duration of short-form videos does not impact engagement. The "Duet" or "Remix" options on TikTok, Instagram, and YouTube serve as a valuable tool for healthcare professionals to counter misinformation. Our study underscores the importance of optimizing educational impact provided by health care professionals at a time when the public increasingly relies on social media for medical information.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".