Cybersecurity Trends in Digital Marketing for Public Health: A PRISMA based Bibliometric Analysis
Bibliographic record
Abstract
This study conducts a bibliometric analysis of digital marketing in public health through the lens of cybersecurity, aiming to evaluate research trends from 2015 to 2025. It identifies key developments, major contributors, and provides guidance for future studies. A total of 1,191 documents were analyzed, revealing a significant annual growth rate of 32.01% and an average of 19.01 citations per document. The analysis explores how digital marketing for public health intersects with cybersecurity, a domain that remains underexplored. Data collection and visualization were conducted using Scopus, Biblioshiny, and VOSviewer, with article selection guided by the PRISMA methodology. Results indicate consistent growth in publications over the decade, though a noticeable decline occurred post-COVID-19 in 2020. The study offers a comprehensive mapping of existing literature and highlights the strategic importance of integrating cybersecurity into digital health marketing to protect patient data, maintain public trust, and enhance health outcomes. It provides valuable insights for researchers, policymakers, and practitioners aiming to improve the security and effectiveness of digital health communication in an increasingly connected world.
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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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.196 | 0.287 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".