Is Education Regulation the Solution to Help Lower Substance Use Rates and Substance Use Disorder in Oregon Youth?
Bibliographic record
Abstract
How can we lower Oregon’s youth substance use disorder (SUD) and overdose rates? Oregon is third in the country for SUD, with 5.77% of adolescents (12-17) having documented SUD. Some claim that it is because of the decriminalization of drugs, like Senator Art Robinson, but another reason relies on how we are educating Oregon youth. Oregon education standards outline requirements for drug and alcohol education as well as Senate Bill 238. By analyzing school districts current prevention programs in high overdose-rate counties (Multnomah, Lane, and Josephine) and low overdose-rate counties (Washington, Yamhill, Linn, Deschutes, Marion, and Clackamas), a lack of quality of education and resources was found. Due to this, a new standard of operations needs to be created that incorporates science-backed education, mental health services, life-skill development, and the creation of an oversight committee. Bardach’s Eightfold Path was used to develop this policy outline having three different outcome options: low (no change), medium (education change), and high (education and resource change), each evaluated on three parts of William Dunn’s evaluative criteria. The strongly preferred option for Oregon schools is the highest prevention measure due to it having the greatest impact on Oregon’s youth futures and current SUD epidemic.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".