Social Media as a Tool for Disseminating Scientific Knowledge on Child Abuse and Resilience: A Brazilian Experience
Bibliographic record
Abstract
Objectives: Social media is a common tool for disseminating information in developing countries, including Brazil. Research regarding social media’s effect on increasing awareness of and knowledge about child abuse has yet to be widely tested in those countries. This exploratory study tested whether social media is a viable outlet for disseminating empirically supported information about child abuse in Brazil. Methods: We utilized social media platforms, such as Facebook, ResearchGate, Twitter (which has subsequently rebranded as X but will be referred to herein as Twitter), Instagram, and YouTube, to disseminate a series of short videos, in cartoon format, on the scientific research surrounding child abuse, adverse childhood events, and resiliency to such experiences. Results: The results indicate that social media has a promising reach in Brazil, as the dissemination started by 10 researchers had over 30,000 views. Conclusion and Implications: Social media may be a viable format for disseminating empirically-supported information in developing countries like Brazil. Each platform, however, has its own characteristics and, as such, the target audiences, engagement, delivery, followers, impact time, and other metrics vary across platforms. Additionally, not all social media platforms provide the same outreach internationally. Future directions are discussed.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".