What Constitutes Prescription Drug Misuse? Problems and Pitfalls of Current Conceptualizations
Bibliographic record
Abstract
Many medications with sedative, anxiolytic, analgesic, or stimulant properties have the potential to be inappropriately used. However, because these substances have beneficial effects, many issues pertinent to understanding prescription drug misuse may differ from those associated with other misused substances. There is currently a lack of consensus about what constitutes prescription misuse and a wide range of operational criteria have been proposed. Inappropriate medication use is frequently defined on the basis of user characteristics (i.e. any non-prescribed use), the reason for use (i.e. use for recreational purposes), the presence of clinically significant symptoms (i.e. meeting diagnostic criteria for abuse and dependence) or on the presence of any of these factors. In cases where multiple criteria are used to define misuse there is often a lack of differentiation among them, while studies that use more specific criteria tend to exclude certain types of misuse from consideration altogether. In addition, in some cases there are a number of potential ways that a single operational criterion can be met and many of these may be associated with substantially different risks, harms, and predictors. Due to considerable variability in the classification of medication misuse both within and between studies, it is currently difficult to interpret the clinical significance of existing findings or to determine the true magnitude of problems associated with any particular form of misuse. In the present review many of the problems and challenges for adequately defining prescription drug misuse will be overviewed and recommendations will be made on how to better characterize this phenomenon.
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 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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".