Protocol for a scoping review on misuse of psychoactive medicines and its consequences
Bibliographic record
Abstract
INTRODUCTION: Misuse of psychoactive medicines, especially prescription opioids, but also benzodiazepines, hypnotics and sedatives, has become a significant public health issue in some countries, especially in the USA, where it has been extensively documented, as well as in Canada and Australia. However, in the European Union (EU) published literature on the topic is scarce and heterogeneous regarding definitions, sources of data, tools and methods of analysis.The aim of this scoping review is to map the key concepts on psychoactive medicines' misuse and examine the existing body of evidence on this topic in the EU. Data on the possible consequences of medicines' misuse-adverse drug reactions, poisonings, hospitalisations and deaths-will also be analysed. METHODS AND ANALYSIS: and the Joanna Briggs Institute. The search strategy, developed by the authors, includes querying three electronic databases-PubMed, Web of Science and Scopus-using keywords and the Medical Subject Headings, for evidence published in English, French, Spanish or Portuguese between 2011 and 2020. Additionally, articles from PubMed alerts and other sources will also be considered. The results of the scoping review will describe the currently available evidence regarding misuse of medicines at EU level. ETHICS AND DISSEMINATION: Since the scoping review methodology focuses on published data, this study does not require ethical approval. We will publish our findings in a peer-reviewed journal and plan to disseminate our work in conferences and scientific meetings. REGISTRATION DETAILS: This scoping review protocol is registered in the Open Science Framework (OSF; see https://osf.io/fzr9u) and has also been shared as a preprint in this free and open-source project management repository. It is available at https://doi.org/10.31219/osf.io/y3s4q.
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 | 0.135 | 0.154 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.160 | 0.046 |
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".