MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueUnderline Science Inc. · 2025
Typeother
Langue
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainable developmentCitationBibliometricsImpact factorPharmacyMillennium Development GoalsWeb of sciencePublishing

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,065
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Bibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,994
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,065
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0210,031
Études des sciences et des technologies0,0010,001
Communication savante0,0040,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,057
Tête enseignante GPT0,397
Écart entre enseignants0,340 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
DomaineÉvaluation
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUnderline Science Inc.Travaux en français237 207