#CBCLIPS: The international impact of a social media-based pediatric surgery interactive educational program
Bibliographic record
Abstract
BACKGROUND: Despite the transformative influence of social media on connectivity, learning, and networking, its role and impact on surgical education remains undefined. In the current study, we evaluated the impact of #CBCLIPS (Case Based Clinical Learning In Pediatric Surgery), a social media-based pediatric surgery educational program. METHODS: A descriptive mixed-methods study evaluated the impact of #CBCLIPS, by analyzing social media engagement metrics (likes, shares, comments) across X, Facebook, and LinkedIn. Additionally, an online survey administered on these platforms assessed participants' perceptions of knowledge gained and satisfaction with the program. Descriptive statistics summarized quantitative data, while thematic analysis was applied to qualitative feedback. RESULTS: #CBCLIPS was highly relevant and appealing to its viewers in all three social media platforms. On X, the episodes boasted an average engagement rate of 6.9 %, significantly outperforming typical benchmarks for the platform. Visibility for #CBCLIPS was substantial on both X and LinkedIn, achieving up to 13,810 and 8282 impressions, respectively. Facebook emerged as the leading platform for interactive engagement, with medians of 60 likes and 13 comments per post, reinforcing its position as the premier platform for fostering community discussions. The survey was completely answered by 160 people from 71 different countries. CONCLUSION: #CBCLIPS is a continuous interactive educational program that significantly engages an international audience, facilitates knowledge acquisition and application, and garners high support among participants. Its reach and success demonstrate the effectiveness of social media-based education in pediatric surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".