Assessing the impact of Canadian primary care research and researchers: Citation analysis.

OBJECTIVE: To describe the citation impact and characteristics of Canadian primary care researchers and research publications. DESIGN: Citation analysis. SETTING: Canada. PARTICIPANTS: A total of 266 established Canadian primary care researchers. MAIN OUTCOME MEASURES: The 50 most cited primary care researchers in Canada were identified by analyzing data from the Scopus database. Various parameters, including the number of publications and citations, research themes, Scopus h index, content analysis, journal impact factors, and field-weighted citation impact for their publications, were assessed. Information about the characteristics of these researchers was collected using the Google search engine. RESULTS: On average, the 50 most cited primary care researchers produced 51.1 first-author publications (range 13 to 249) and were cited 1864.32 times (range 796 to 9081) over 29 years. Twenty-seven publications were cited more than 500 times. More than half of the researchers were men (60%). Most were clinician scientists (86%) with a primary academic appointment in family medicine (86%) and were affiliated with 5 universities (74%). Career duration was moderately associated with the number of first-author publications (0.35; P=.013). Most research focused on family practice, while some addressed health and health care issues (eg, continuing professional education, pharmaceutical policy). CONCLUSION: Canada is home to a cadre of primary care researchers who are highly cited in the medical literature, suggesting that their work is of high quality and relevance. Building on this foundation, further investments in primary care research could accelerate needed improvements in Canadian primary care policy and practice.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis.

BACKGROUND: ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. OBJECTIVE: This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. METHODS: This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. RESULTS: A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. CONCLUSIONS: Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

The impact of US-China tensions on US science: Evidence from the NIH investigations.

Amid the discourse on foreign influence investigations in research, this study examines the impact of NIH-initiated investigations starting in 2018 on U.S. scientists' productivity, focusing on those collaborating with Chinese peers. Using publication data from 2010 to 2021, we analyze over 113,000 scientists and find that investigations coincide with reduced productivity for those with China collaborations compared to those with other international collaborators, especially when accounting for publication impact. The decline is particularly pronounced in fields that received greater preinvestigation NIH funding and engaged more in U.S.-China collaborations. Indications of scientist migration and broader scientific progress implications also emerge. We also offer insights into the underlying mechanisms via qualitative interviews.

Analysis of justification for author order and gender bias in author order among those contributing equally.

The practice of designating two or more authors as equal contributors (ECs) on a scientific publication is increasingly common as a form of sharing credit. However, EC authors are often unclearly attributed on curriculum vitae (CVs) or citation engines, and it is unclear how research teams determine author order within an EC listing. In response to studies showing that male authors were more likely to be placed first in an EC listing, the American Society for Microbiology (ASM) required that authors explain the reasons for author order beginning in 2020. In this study, we analyze data from over 2,500 ASM publications to see how this policy affected gender bias and how research teams are making decisions on author order. Data on publications from 2018 to 2021 show that gender bias was largely nonsignificant both before and after authors were asked by ASM to provide an EC statement. The most likely reasons for EC order included alphabetical order, seniority, and chance, although there were differences for publications from different geographic regions. However, many research teams used unique methods in order selection, highlighting the importance of EC statements to provide clarity for readers, funding agencies, and tenure committees. IMPORTANCE: First-author publications are important for early career scientists to secure funding and educational opportunities. However, an analysis published in eLife in 2019 noted that female authors are more likely to be placed second even when both authors report they have contributed equally. American Society for Microbiology announced in response that they would require submissions to include a written justification of author order. In this paper, we analyze the resultant data and show that laboratories are most likely to use some combination of alphabetical order, seniority, and chance to determine author order. However, the prevalence of these methods varies based on the research team's geographic location. These findings highlight the importance of equal contributor statements to provide clarity for readers, funding agencies, and tenure committees. Furthermore, this work is critically important for understanding how these decisions are made and provides a glimpse of the sociology of science.

Gender-diverse teams produce more novel and higher-impact scientific ideas.

Science's changing demographics raise new questions about research team diversity and research outcomes. We study mixed-gender research teams, examining 6.6 million papers published across the medical sciences since 2000 and establishing several core findings. First, the fraction of publications by mixed-gender teams has grown rapidly, yet mixed-gender teams continue to be underrepresented compared to the expectations of a null model. Second, despite their underrepresentation, the publications of mixed-gender teams are substantially more novel and impactful than the publications of same-gender teams of equivalent size. Third, the greater the gender balance on a team, the better the team scores on these performance measures. Fourth, these patterns generalize across medical subfields. Finally, the novelty and impact advantages seen with mixed-gender teams persist when considering numerous controls and potential related features, including fixed effects for the individual researchers, team structures, and network positioning, suggesting that a team's gender balance is an underrecognized yet powerful correlate of novel and impactful scientific discoveries.