Public Engagement with Dermatology Contents on <i>Facebook</i>
Bibliographic record
Abstract
BACKGROUND: The gtring presence of dermatology platforms on Facebook has been acknowledged; however, little is known about the extent to which different types of content influence the level of engagement with online users. OBJECTIVE: To assess the level of public engagement with different types of content posted on Facebook pages devoted to dermatology. METHODS: A search on Facebook identified existing pages for dermatology academic journals, professional societies, and patient-centered groups. Then the engagement rate was calculated for each content type published on the selected pages. RESULTS: The median engagement rates were 63.8% for educational posts, 41.3% for interactive posts, 27.4% for news articles, 11.8% for academic articles, and 9.3% for others. CONCLUSION: Educational posts engaged with online users the most effectively. The level of engagement is a key determinant of knowledge dissemination via online tools, and the type of content may influence the level of engagement.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".