This integrative article explored how nurse-researchers craft and negotiate researcher identity through reflexivity. Drawing on three reflexive narratives situated across distinct practice and career contexts, including academe and military nursing, this article synthesized how personal histories, professional roles, and methodological training intersect in order to shape how one becomes a researcher. Guided by Benner's novice-to-expert lens and contemporary discussions on positionality and reflexive practice, this article's synthesis surfaced recurring themes: identity-in-motion rather than identity-as-status; persistence and vulnerability as engines of growth; insider–outsider movements and the ethical responsibilities being invited; and methodological pluralism as a value stance that links rigor with relevance and care. The narratives also illuminated how institutional cultures, resource constraints, and role expectations contour opportunities for inquiry, while reflexive writing functions as both method and pedagogy, supporting integrity, accountability, and epistemic fluency. It was argued in this article that cultivating researcher identity in nursing requires spaces that normalize doubt, foreground values, and make visible the relational, moral, and political textures of knowledge production. Implications included embedding explicit reflexivity and positionality work in curricula, mentoring, and research supervision; recognizing mixed-methods and qualitative approaches as complementary pathways to impact and to align institutional supports with nurses' dual commitments for both practice and scholarship. In this article, the process of becoming a nurse-researcher had been shown to be a continual practice of courage, reflexivity, and commitment that is by doing it, and learning it, until nurse-researchers make it.

Human ; Research ; Nursing

BACKGROUND

Financial hardship is a defining reality for many nursing students in the Philippines. While nursing education is viewed as a path to family upliftment, the academic burden of sustaining clinical fees, requirements, and living expenses can be overwhelming.

This study explored the lived experiences of Filipino nursing students who face financial strain, while focusing on its sources such as emotional and psychological impacts, coping and survival strategies, and academic consequences.

A descriptive qualitative design was employed to capture rich, contextualized narratives from sixteen Bachelor of Science in Nursing (BSN) students enrolled at a state university in the Bicol Region. Data was collected through semi-structured interviews and were analyzed thematically using Braun and Clarke's six-phase approach. Trustworthiness was ensured through member checking, audit trails, and reflexive journaling.

Four themes emerged: (1) Sources of Financial Strain—family income instability, solo-parent dependency, family medical expenses, and multiple dependent siblings; (2) Emotional and Psychological Impacts—hopelessness, anxiety, and depression; (3) Coping and Survival Strategies—working while studying and sacrificing rest or self-care; and (4) Academic Consequences—difficulty focusing and fear of dropping out. These findings revealed a complex interplay of economic, emotional, and cultural factors that shape students' struggle to persist.

Financial strain among Filipino nursing students extends beyond economic limitation to encompass moral obligation, emotional fatigue, and academic compromise. Universities must provide holistic support—financial, psychosocial, and institutional—to ensure that nursing students not only survive but succeed.

This reflexivity paper explores the evolving identity of a nurse leader turned researcher, tracing a journey along “the road less travelled” in nursing. It examines how personal history, professional experiences, and sociocultural contexts shape the researcher's positionality and influence the pursuit of knowledge. Anchored in critical, feminist, and interpretivist paradigms, the paper underscores reflexivity as both an ethical stance and a transformative process that connects power, purpose, and praxis. Reflective practice bridges experience and empirical knowledge (Sherwood & Webb, 2024). Through self-analysis, the author illuminates how lived experiences, leadership practice, and gendered perspectives inform inquiry, ultimately advancing a vision of nursing research that is self-aware, socially responsive, and justice-oriented.

The guidelines for clinical research on new drugs provide unified standards for drug developers, researchers, and regulatory authorities, playing a crucial role in new drug development. This article systematically reviews the evolution of guidelines for clinical research on new traditional Chinese medicine(TCM) drugs in China, with a focus on analyzing the current status of these guidelines and the problems that exist. It also provides interpretations of three important guidelines. The article points out that with the continuous emergence of new clinical trial design methods, development concepts, and tools, and under the background of the "three combinations" evidence evaluation system for new TCM drugs, it is imperative to revise existing guidelines, formulate new ones, and develop new tools for clinical efficacy evaluation. It is hoped that relevant departments will adopt an open attitude and work together to build a technical system of clinical research guidelines for new TCM drugs that aligns with the characteristics of TCM.
Humans ; Biomedical Research/trends* ; China ; Clinical Trials as Topic ; Drugs, Chinese Herbal/therapeutic use* ; Guidelines as Topic ; Medicine, Chinese Traditional/standards*

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.
Artificial Intelligence ; Bibliometrics ; Humans ; China ; Equipment and Supplies ; United States ; Biomedical Research

Biomedical data is surging due to technological innovations and integration of multidisciplinary data, posing challenges to data management. This article summarizes the policies, data collection efforts, platform construction, and applications of biomedical data in China, aiming to identify key issues and needs, enhance the capacity-building of platform construction, unleash the value of data, and leverage the advantages of China's vast amount of data.
China ; Humans ; Biomedical Research ; Data Management ; Data Collection

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

BACKGROUND: Supporting the mental health of researchers is essential to maintaining human resources and advancing science. This study investigated the association between Japanese medical researchers' perceptions of research evaluation processes and their psychological well-being. METHODS: We performed a web-based self-administered questionnaire survey. The questionnaires were distributed to each academic society through the Japanese Association of Medical Sciences from December 2022 to January 2023. These questionnaires targeted medical researchers. Exposure was the medical researchers' perceptions of quantitative indicators for evaluating medical research and researchers. The outcome was psychological well-being, measured using the Japanese version of the World Health Organization-Five Well-Being Index (WHO-5). Multivariable-adjusted logistic regressions were conducted to investigate the association between individual attitudes toward research evaluation and psychological well-being. Stratified analyses by research fields, i.e., clinical, basic, and social medicine, were also performed. RESULTS: A total of 3,139 valid responses were collected. After excluding 176 responses from research fields of other than clinical, basic, or social medicine, 2,963 researchers (2,185 male, 737 female, and 41 other) were analyzed. Prevalence of poor well-being (WHO-5 score <13) was 28.3% in the researchers. The highest number of medical researchers was in clinical medicine (n = 500) followed by basic medicine (n = 217) and social medicine (n = 121). Medical researchers who considered research funding slightly important/not important for researcher evaluation had poorer psychological well-being than those who considered it especially important (slightly important: adjusted odds ratio (aOR) 1.33, 95% confidence interval (CI) 1.03-1.71; not important: aOR 1.53, 95%CI 1.10-2.12). This tendency was stronger among basic medical researchers than clinical or social medical researchers. The research field significantly modified the relationship between research funding received and interaction with poor psychological well-being both additively (P = 0.030) and multiplicatively (P = 0.024). CONCLUSIONS The discrepancy between medical researchers' attitudes toward research evaluation and the current state of research evaluation in their research community may worsen their psychological well-being. The influence of this discrepancy differs among clinical, basic, and social medicine. Appropriate evaluation of medical research and researchers in each field can facilitate improving their psychological well-being via the resolution of this discrepancy.
Humans ; Japan ; Female ; Male ; Cross-Sectional Studies ; Adult ; Research Personnel/statistics & numerical data* ; Middle Aged ; Biomedical Research ; Surveys and Questionnaires ; Mental Health ; Psychological Well-Being ; East Asian People