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Why we should still estimate the costs of substance abuse even if we needn't pay undue attention to the bottom line

2009· article· en· W1576949414 on OpenAlexaff
Eric Single

Bibliographic record

VenueDrug and Alcohol Review · 2009
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Drug Research Institute
KeywordsSubstance abuseAddictionPublic economicsPresentation (obstetrics)MedicineCriminologyPsychiatryPsychologyEnvironmental healthPolitical scienceEconomicsActuarial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.352
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2009
Admission routes1
Has abstractyes

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