Models of addiction and types of interventions: An integrative look
Bibliographic record
Abstract
Simon, R., & West, R. (2015). Models of addiction and types of interventions: An integrative look. The International Journal Of Alcohol And Drug Research, 4(1), 13-20. doi:http://dx.doi.org/10.7895/ijadr.v4i1.198Background: Use of psychoactive substances and problem gambling create serious harm to individuals who engage in these practices and to society as a whole (World Health Organization, 2002). The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) regularly monitors drug-related problems and interventions, as well as the efficiency of interventions. The scope and methodology of monitoring, however, depends on the conceptualization of “addiction.”Methods: The relevant literature was screened for models and theories relating to “addiction,” resulting in a systematic overview of the concepts and related approaches (EMCDDA, 2013). Using this as a background, different approaches for interventions and their theoretical bases are discussed.Results: Models of addiction follow two approaches. Most of these focus on the individual addict, involving constructs such as emotions, drive states, habits, choice, and goal-oriented processes, or else taking a more integrative or change-oriented view. Others are population-based models, including social network, economic, communication, and organizational system models.While substance- and non-substance-related addictions differ in a number of respects, they share key elements: a repeated powerful motivation to engage in a particular behavior, acquired through enacting the behavior, despite the experience or risk of significant harm. Nine different types of intervention to combat addiction found in the literature involve attempts to change one or more of three factors that interact to underpin behavior: capability, opportunity, and motivation (the “COM-B” model). The models of addiction reviewed may serve as a basis for such interventions.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.011 | 0.032 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".