Publications

Retractions are an essential mechanism for correcting the scientific record, yet retraction notices do not always fully explain the circumstances behind the decision. This study assessed the quality of communication in retraction notices published by Latin American journals included in the SCImago Journal & Country Rank up to 2025. Of the 1119 journals examined, we identified 144 retracted articles in Scopus, corresponding to 139 unique retraction notices. The assessment comprised six criteria grouped into two components: access and traceability, and content informativeness and transparency. We also examined several features of the retraction process as reported in the notices. In 77.0% of cases, editorial bodies were the only entities identified in the notices as retracting the article. In 47.5% of cases, the notices did not specify the origin of the alert, request, or process leading to retraction, while 22.3% attributed it to third parties. Only 30.9% explicitly reported an investigation, and 58.3% reported contact with the authors. The mean overall score was 9.15 out of 12 (SD = 1.78). The highest scores were observed for article identification, notice availability, and clarity of the reason for retraction, whereas the lowest were found for specificity of the content or scope affected and transparency regarding the authors’ position. Overall, the findings show that notice availability and article identification alone do not ensure complete reporting of the circumstances surrounding a retraction.

This bibliometric study examines research that explicitly combines sparse methods with Tucker-related techniques. Using PRISMA 2020 as a reporting guide for record identification and selection, we retained 254 journal articles indexed in Scopus and Web of Science and published between 2000 and May 2025. The analysis covers annual publication output, raw citation counts, international collaboration, and keyword co-occurrence using VOSviewer (version 1.6.20) and R (version 4.4.2). The 254 articles show sustained growth in publication output between 2010 and 2024. China and the United States account for a substantial share of the retrieved literature, while Pattern Recognition and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing are prominent within this specific dataset. Because citation counts vary with publication year, discipline, and database coverage, they are interpreted as descriptive indicators rather than direct measures of research quality or normalized impact. The retrieved studies include applications in neuroscience, finance, hyperspectral image analysis, and multimodal learning. The co-occurrence maps center on terms such as tensor decomposition, low-rank approximation, and deep learning. These results describe how sparse and Tucker techniques are being used across several areas of data science, while remaining specific to the corpus defined by the search strategy.

Generative artificial intelligence (GenAI) has rapidly entered undergraduate research training, yet its relationship with research self-efficacy and attitudes toward scientific research remains underexplored among Health Sciences students. This cross-sectional study examined how self-reported GenAI literacy and research self-efficacy are associated with attitudes toward scientific research, separately and when modelled together. Of 101 submissions from undergraduate Health Sciences students enrolled in research courses at a private Peruvian university, 91 were analysed after 10 invariant response patterns were excluded. Students completed a 36-item self-report questionnaire whose items had been reviewed for content relevance and clarity by three experts. Analyses combined Spearman correlations, multiple regression with HC3 standard errors, a bootstrapped indirect-effect model and exploratory fuzzy-set qualitative comparative analysis (fsQCA). Reliability computed from the three dimension scores of each construct was α = 0.861 for GenAI literacy, 0.927 for research self-efficacy and 0.790 for attitudes. GenAI literacy correlated with attitudes (ρ = 0.475, Holm-adjusted p < 0.001), but in the female–male regression model (n = 90), research self-efficacy was the only one of the two capability scores that retained a statistically significant adjusted association (b = 0.353, 95% CI 0.087 to 0.619, p = 0.010; GenAI literacy b = 0.088, 95% CI −0.214 to 0.389, p = 0.564; R² = 0.405). The indirect term through research self-efficacy (0.313, 95% CI 0.092 to 0.536) is a statistical decomposition of overlapping, contemporaneous scores rather than evidence of a mechanism. Discriminant validity between GenAI literacy and research self-efficacy was not established (HTMT = 0.912), and parallel analysis favoured two factors rather than three. Under the primary calibration (theoretical anchors 2-3-4 with crossover memberships assigned to 0.501), functional knowledge and the critical–ethical evaluation of GenAI appeared in both terms of the intermediate fsQCA solution (consistency = 0.913, coverage = 0.741), but the minimised solutions changed with alternative anchors and with the handling of cases at the crossover. The findings describe associations among self-reported measures; whether developing both capabilities jointly improves research attitudes remains a question for future evaluation.