The Fentanyl System Shock – Are There Lessons to Learn From the COVID-19 System Shock Framework?; Comment on "The COVID-19 System Shock Framework: Capturing Health Sys-tem Innovation During the COVID-19 Pandemic"
Bibliographic record
Abstract
The Sydney Children's Hospitals Network (SCHN) addressed the challenges of the COVID-19 pandemic by implementing innovative changes which made their health system resilient and responsive. For other healthcare systems, there are important takeaways. In the United States and Canada, an urgent widespread response is needed to address the overdose crisis, driven by potent synthetic opioids (ie, fentanyl and its derivates). We project the COVID-19 System Shock Framework (CSSF) on to the North American healthcare systems and suggest a Fentanyl System Shock Framework, which provides a framework for necessary changes and innovations to address the overdose crisis. To become resilient to the fentanyl system shock, core components as well as overarching values, health policy, and online technologies need to be adapted to reduce the death count and meet the evolving needs of marginalised individuals who use opioid. Future research should focus on scientifically assessing such implementations to guide evidence-based decision making.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.077 | 0.069 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".