Why we should still estimate the costs of substance abuse even if we needn't pay undue attention to the bottom line
Bibliographic record
Abstract
A coalition of provincial, national and international addictions agencies has sponsored a series of symposia leading to the developing of international guidelines for estimating the costs of substance abuse. These guidelines have now been used in national studies in four continents, with more consistent and comparable results than in previous studies. Although the bottom-line results have been used to argue for alcohol and drug issues having a higher place on the public policy agenda, the real value in such studies lies in the detailed results regarding mortality and morbidity attributable to substance abuse, the relative contribution of acute versus chronic conditions to overall problem levels and the role of substance misuse in adverse social consequences, such as crime and economic productivity. There is a variety of factors which undermine the robustness of the findings, including lack of data, layering of assumptions and changes in the epidemiological knowledge base. It is argued that economic cost estimates should nonetheless be conducted and continually refined, as the detailed findings are of great utility to the design and targeting of prevention programming and policy. The presentation concludes on a personal note of farewell, as this is the author's final conference presentation.
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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 | 0.029 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".