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Record W7115705956 · doi:10.48448/yn0a-sg03

[V] Publication Trends on Priority Epidemics According to the Sustainable Development Goals in Pharmaceutical Journals, 2000-2024

2025· other· W7115705956 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentCitationBibliometricsImpact factorPharmacyMillennium Development GoalsWeb of sciencePublishing

Abstract

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Julia Soto Rizzato,1 Marcus Tolentino Silva,2 David Moher,3 Tais Freire Galvão1 Objective To assess publication trends in epidemics estimated to end by 2030 according to the Sustainable Development Goals (SDGs) in pharmaceutical journals in the past 25 years and the association of the adoption of these SDGs in 2015 with this trend. Assessments of the transition from Millennium Development Goals to SDGs are available,1 but investigations are lacking on the impact of the adoption of these SDGs on publications of a specific target. Design This cross-sectional study assessed all journals listed in the Pharmacology and Pharmacy category of the Journal Citation Reports. The primary outcome was the proportion of articles published addressing AIDS, tuberculosis, and malaria—epidemics targeted to end according to SDG 3.3. We consulted the Clarivate Web of Science Core Collection on December 19, 2024, to identify the total number of articles and the number of publications in each journal from 2000 to 2024 related to these epidemics. Data on country were collected from Web of Science metadata, which are based on authors’ affiliations and then classified into low-, middle-, or high-income countries based on World Bank country classifications by income level. A paper was considered from a low- or middle-income country if at least 1 author was from 1 of the countries in the metadata. An interrupted time series analysis was conducted to calculate the regression coefficient (β) and 95% CI of the proportion of papers (per 1000 publications) on SDG 3.3 before and after its adoption in 2015. Stata, version 14.2 was used for statistical analyses. Results Three hundred fifty-five journals were included, which published 1,313,671 articles, of which 49,687 (3.8%) addressed SDG 3.3 from 2000 to 2024 and 37,521 (2.9%) were published by authors from high-income countries and 12 166 (0.9%) were published by authors from low- and middle-income countries. Between 2000 and 2015, there was an overall growing trend in published papers related to SDG 3.3 (β, 0.47; 95% CI, 0.18-0.76; P = .003), which was more pronounced in developed countries (β, 0.69; 95% CI, 0.38-1.00; P < .001), while the trend from developing countries was not significant (β, 0.21; 95% CI, −0.19 to 0.06; P = .30) (Figure 25-0901). After the adoption of SDGs in 2015, SDG 3.3 publications started to decrease until 2024 (β, −1.00; 95% CI, −1.54 to −0.47; P = .001) in a similar pattern in highincome countries (β, −1.40; 95% CI, −2.00 to −0.81; P < .001), whereas a nonsignificant increase was observed in lower-income countries (β, 0.05; 95% CI, −0.05 to −0.73; P = .89) in the same period. https://assets.underline.io/markdown_image/1/image/3a82bbfa1f2eb9d809c2927e383599b4.png Conclusions The adoption of SDGs in 2015 did not seem to affect the publication priorities of pharmaceutical journals until 2024, which indicates that these measures may not have stimulated research efforts and publications. Even when considering that the results indicating research agenda priorities would take a few years to be published, the trend decreased until the end of the series, almost a decade after the adoption of SDGs. Reference 1. Díaz-López C, Martín-Blanco C, De la Torre Bayo JJ, Rubio-Rivera B, Zamorano M. Analyzing the scientific evolution of the sustainable development goals. Appl Sci. 2021;11(18):8286. doi:0.3390/app11188286 1Faculdade de Ciências Farmacêuticas, Universidade Estadual de Campinas, Campinas, Brazil, taisgalvao@gmail.com; 2Faculdade de Ciências de Saúde, Universidade de Brasília, Brasília, Brazil; 3Centre for Journalology, Methods Centre of the Ottawa Hospital Research Institute, Ottawa, Canada. Conflict of Interest Disclosures David Moher and Tais Freire Galvão report being advisory board members of the International Congress on Peer Review and Scientific Publication but were not involved in the review or decision for this abstract. The other authors declare no conflict of interest. Funding/Support This study was funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (Finance Code 001, granted to Julia Soto Rizzato). Tais Freire Galvão receives a productivity scholarship from the National Council for Scientific and Technological Development (grant 313431/2023-00). Role of the Funder/Sponsor The funders had no role in this research.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0210.031
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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.

Opus teacher head0.057
GPT teacher head0.397
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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