The Public Health Approach to the Worsening Opioid Crisis in the United States Calls for Harm Reduction Strategies to Mitigate the Harm From Opioid Addiction and Overdose Deaths
Bibliographic record
Abstract
The opioid crisis has devastated the U.S. more than any other country, and the epidemic is getting worse. While opioid prescriptions have decreased by more than 40% from its peak in 2010, unfortunately, opioid-related overdose deaths have not declined but continued to increase. With greater scrutiny on prescription opioids, many users switched to the cheaper and more readily available heroin that drove up heroin-related overdose deaths from 2010 to peak in 2016, being overtaken by the spike in synthetic opioid (mostly fentanyl)-related overdose deaths. The surge in fentanyl-related overdose deaths since 2013 is alarming as fentanyl is more potent and deadly. One thing is certain the opioid crisis is not improving but has become dire with the surge in fentanyl-related overdose deaths. Evidence-based strategies have to be implemented in the U.S. to control this epidemic before it destroys more lives. Other countries, including European countries and Canada, have invested more in harm reduction strategies than the U.S. even though they (especially Europe) do not face anywhere near the level of crisis as the U.S. In the long-run, upstream measures (tackling the social determinants of health) are more effective public health strategies to control the epidemic. In the meantime, however, harm reduction strategies have to be employed to mitigate the harm from addiction and overdose deaths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.029 | 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".