The Role of Primary Care Physicians in Curtailing Harmful Social Media Trends
Bibliographic record
Abstract
Social media platforms, such as YouTube and Instagram, have become the latest medium for communication with a vast potential for influencing society. With their rise, a virtual market now exists where attention in the form of "likes," "views," and "followers" is traded for a monetary and psychological benefit. Amid this trade, physically risky behaviors have arisen to become a new attraction for attention, leading to numerous "trends" that encourage the same risk-taking behavior. Such trends, even those with a positive goal, have simultaneously led to injuries and fatalities, which highlights the necessity of a proactive approach to curtail the same. While media outlets and some non-governmental organizations usually highlight the risks of participating in these trends, the healthcare community has yet to have a collective and organized response to extreme social media participation. As such, a collaborative effort involving multiple tiers of the healthcare community is required to successfully prevent vulnerable populations from falling prey to the virtual attention-based economy of extreme social media participation.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".