Policy implications of marked reversals of population life expectancy caused by substance use
Bibliographic record
Abstract
BACKGROUND: Life expectancy has been increasing steadily over the past century in most countries, with only a few exceptions such as during wartimes. DISCUSSION: Marked reversal of life expectancy has been linked to substance use and related policies. Three such examples are discussed herein, namely the double reversal of life expectancy trends (first to positive, then to negative) associated with reducing alcohol supply in the then Union of Soviet Socialist Republics (USSR), followed by a rapid increase in availability; the impact of the rapid increase of prescription opioids on white non-Hispanics in the US; and the systemic impact of the violence accompanying the drug war in Mexico on the life expectancy of men. Alcohol policies were crucial to initiate the positive reversal in the USSR, and different substance use policies could have avoided the negative impacts on life expectancy of the described large groups or nations. Substance use policies can be responsible for abrupt negative changes in life expectancies. An orientation of such policies towards the goals of public health and societal well-being can help avoid such changes.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".