Bibliographic record
Abstract
A variety of interventions, both therapeutic and preventive, have been used to control, reduce or eliminate substance use and misuse and their attendant problems. Yet, despite years of ever more sophisticated and expensive ways of responding to the use and misuse of a variety of legal and illegal substances, addiction continues to be experienced as a major social problem that plagues users, their families and communities, therapists and clinicians, policymakers, and the public. In view of a recidivist treatment population and finite resources, this paper considers whether and to what extent treatments and other interventions used for planned interventions with alcohol- and drug-use related problems work. It examines both clinical treatment outcomes and broad-based population prevention interventions, and reviews their underlying rationales. Finally, it identifies a number of areas that must be addressed if we are to improve the situation. These areas include a lack of agreement on what is meant by the problem of "addictions," how successful interventions are to be defined and measured so that better interventions can be applied in the future, as well as integrating the processes of quality and appropriateness into the planning, implementation and assessment of effective, needed substance use intervention.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".