Examining the evolution and impact of OTC vending machines in Global Healthcare Systems
Bibliographic record
Abstract
The study of over the counter (OTC) vending machines is crucial given their growing popularity and potential impact on the pharmaceutical industry and consumer behaviour. This study involves a bibliometric quantitative analysis of academic literature to evaluate OTC vending machines in terms of their evolution, current trends, and potential areas for future research . The Scopus database was searched using its advanced search tool, focusing on papers that included the search query in their titles, abstracts, and keywords. Data analysis included bibliometric indicators such as publication counts, citation trends, and co-authorship networks, which were visualized using VOSviewer software (version 1.6.20) to highlight key research themes and collaboration patterns. A total of 399 publications on OTC vending machines were found between 1833 and 2024. Over the last 20 years, there has been an annual increase in the number of publications related to OTC vending machines, rising from 1 in 2001 to 31 in 2023. The United States ( n = 118; 29.57 %) led in productivity, followed by the United Kingdom (45; 11.27 %), India (30; 7.51 %), Australia (27; 6.76 %), Canada (16; 4 %), Italy (15; 3.75 %), and China (15; 3.75 %). A total of 35 institutions have been involved in research on OTC vending machines. The Dubai Municipality contributed the highest percentage of articles ( n = 3, 0.75 %), followed by the Emirates Health Services (n = 3, 0.75 %), Al Ain University ( n = 2, 0.5 %), and Baystate Medical Center (n = 2, 0.5 %). Before 2016, much of the research on OTC vending machines focused on terms related to healthcare policy and health promotion, indicating the early exploration of this field. Present trends highlight terms associated with pharmacy practice, such as pharmacists, pharmacy, and prescription-related subjects. This study emphasises the practical necessity for enhanced regulatory structures to mitigate risks such as medication abuse, unfavourable drug interactions, and incorrect dispensing practices. Additionally, the study highlights the need for interdisciplinary collaboration among technologists, policymakers, and healthcare professionals to maximize the benefits of OTC vending machines while addressing consumer behaviour and safety issues. • A review of OTC vending machines explored past trends, current state, and predicted future research hotspots. • Vending machines enhance healthcare accessibility; academia-industry collaboration drives safer, user-friendly innovations. • Ensuring safety and regulatory compliance is key to prevent misuse; policies must enforce strict rules and guidelines.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.077 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".