The Prescription Opioid Epidemic: Social Media Responses to the Residents’ Perspective Article
Bibliographic record
Abstract
In June 2014, Annals of Emergency Medicine collaborated with the Academic Life in Emergency Medicine (ALiEM) blog-based Web site to host an online discussion session featuring the Annals Residents' Perspective article "The Opioid Prescription Epidemic and the Role of Emergency Medicine" by Poon and Greenwood-Ericksen. This dialogue included a live videocast with the authors and other experts, a detailed discussion on the ALiEM Web site's comment section, and real-time conversations on Twitter. Engagement was tracked through various Web analytic tools, and themes were identified by content curation. The dialogue resulted in 1,262 unique page views from 433 cities in 41 countries on the ALiEM Web site, 408,498 Twitter impressions, and 168 views of the video interview with the authors. Four major themes about prescription opioids identified included the following: physician knowledge, inconsistent medical education, balance between overprescribing and effective pain management, and approaches to solutions. Free social media technologies provide a unique opportunity to engage with a diverse community of emergency medicine and non-emergency medicine clinicians, nurses, learners, and even patients. Such technologies may allow more rapid hypothesis generation for future research and more accelerated knowledge translation.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".