Understanding the Opioid Overdose Crisis: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
Background and Objective: Opioid overdose represents a critical public health issue that has attracted considerable research interest. Therefore, objective of this study aimed to conduct a comprehensive analysis of the opioid overdose research (OOR) landscape, performance, evolution, citation impact and changing research themes. Materials and Methods: A bibliometric analysis was performed on the OOR data published between, 1971 and 2024. Data were gathered from the Scopus database and analyzed using Bibliometrix and VOSviewer. Citation counts, prolific authors, top sources, and seminal publications were also identified. Thematic mapping was performed to aid the visualization of the major clusters in the OOR data. Results: The analysis included 13,971 authors who contributed to the OOR. Prolific authors, such as Walley, A.Y., and Green, T.C., made notable contributions. "Drug and alcohol dependence" emerged as the top source in the publications. The United States exhibited the highest research output, followed by Canada, the United Kingdom, and Australia. The average citation count per article was 22.44, indicating the impact and visibility of the research. Seminal publications have addressed critical topics including opioid prescription patterns, economic burden, medication-assisted therapies, and overdose prevention programs. Thematic mapping revealed clusters related to drug overdose, opioids, overdose prevention, toxicology, buprenorphine, and opioid use disorders. Conclusion: The findings showed an ongoing need for further studies on gap filling, such as long-term consequences, socioeconomic factors and inequalities, in addition to technology application and interdisciplinary collaboration. By focusing on new themes, as well as longitudinal studies, stakeholders can gain improved knowledge on opioid overdose problem-solving solutions that have been applied to victims and their communities, thereby improving outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.035 | 0.024 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".