Research integrity and peer review最新文献

{"title":"Correction: Who deserves credit, who receives credit? A cross-sectional survey on the handling of co-authorship in medical dissertations in Germany.","authors":"Laura Klempp, Nadin Zerin Tanriverdi, Adrian Loerbroks, Niklas Juth, Nils Hansson","doi":"10.1186/s41073-026-00237-8","DOIUrl":"10.1186/s41073-026-00237-8","url":null,"abstract":"","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13404578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why researchers cut corners: motivations, pressures, and personality in questionable research practices.","authors":"Robin Brooker","doi":"10.1186/s41073-026-00229-8","DOIUrl":"10.1186/s41073-026-00229-8","url":null,"abstract":"<p><strong>Background: </strong>Some questionable research practices (QRPs) are regarded as core contributors to problems of reproducibility and replication, while others compromise the integrity and credibility of research by obscuring responsibility and incentives. However, evidence on the individual-level drivers of QRP engagement is fragmented. This study examines theory-informed associations between social and psychological factors and self-reported QRP engagement.</p><p><strong>Methods: </strong>Using data from a cross-sectional survey of researchers across disciplinary fields (18,376 invited; N = 3,050 respondents), this study examines associations between social and psychological factors and self-reported QRP engagement. The sample reflects a self-selected subset of invited respondents.</p><p><strong>Results: </strong>I found that commitment to universalism (β = -0.14, 95% CI [-0.18, -0.10]), communalism (β = -0.04, 95% CI [-0.08, 0.0]), and organised scepticism (β = -0.15, 95% CI [-0.2, -0.1]) - as well as higher conscientiousness (β = -0.07, 95% CI [-0.11, -0.03]), agreeableness (β = -0.03, 95% CI [-0.07, 0.00]), emotional stability (opposite to neuroticism) (β = -0.07, 95% CI [-0.11, -0.04]), and openness (β = -0.08, 95% CI [-0.12, -0.04]) - were negatively associated with average self-reported QRP engagement. Publication pressure was associated with greater average self-reported QRP engagement (β = 0.12, 95% CI [0.08, 0.16]), but this association was weaker among respondents with higher scores on a factor interpreted as endorsement of universalism (β = -0.04, 95% CI [-0.08, 0.00]). Funding pressure was not statistically associated with self-reported average QRP engagement. Motivations to falsify theories and findings (β = -0.06, 95% CI [-0.11, -0.01]), and to improve impact and quality of results (β = -0.11, 95% CI [-0.15, -0.06]), were negatively associated with self-reported QRP engagement. Conversely, ego and esteem-related motivation (e.g., fame, achieving social and political goals) was found to have a direct association with mean self-reported QRP engagement (β = 0.11, 95% CI [0.07, 0.15]), whereas career-growth motivations showed no clear direct association (β = -0.03, 95% CI [-0.07, 0.01]). However, both were indirectly associated with QRP engagement through scientific achievement and impact/quality motivations.</p><p><strong>Conclusions: </strong>These findings highlight specific social and psychological factors that are associated with self-reported QRP engagement, offering potential leverage points for efforts to promote research integrity and reduce questionable practices. However, because the analytic sample reflects a self-selected subset of invited researchers and is drawn largely from Europe and the United States, the findings may not fully generalise to the broader global research community.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":" ","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13670749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT).","authors":"Szymon M Drobniak, Martyna Cendrowska-Pek, Agnieszka Gudowska, Katarzyna Janas, Paweł Podkowa, Karolina Skorb, Monika Gronowska, Wioleta Oleś, Faina Bikmurzina, Natalia Boroń, Magdalena Zagalska-Neubauer, Shinichi Nakagawa, Małgorzata Łagisz, Aneta Arct, Joanna Rutkowska","doi":"10.1186/s41073-026-00230-1","DOIUrl":"10.1186/s41073-026-00230-1","url":null,"abstract":"<p><p>Generative artificial intelligence (AI) is rapidly becoming embedded across scientific workflows, yet mechanisms for transparently documenting its use remain fragmented and weakly enforced. Focusing on ecology and evolutionary biology as a model discipline, we systematically mapped AI-related journal policies across 230 journals and assessed article-level compliance using a large sample of recent publications. To provide a reporting background, we also synthesised author contribution guidelines. Nearly half of journals provided no guidance on AI use, and where policies existed, they were largely generic, publisher-driven, and poorly translated into reporting practice. While author contribution statements were widely adopted, explicit AI disclosures appeared in fewer than 6% of papers, even in journals with formal AI policies. Text-mining of 124 guideline documents revealed highly standardised, precautionary language emphasising responsibility and prohibitions, with minimal operational guidance on acceptable uses or disclosure formats. To address this gap, we introduce AIdIT (AI disclosure for Improved Transparency), a standardised, taxonomy-based framework for reporting AI use across all stages of the research lifecycle. AIdIT integrates structured categories of AI use, human oversight statements, and machine-readable outputs to support reproducibility, accountability, and comparability. Together, our systematic evidence synthesis and proposed framework highlight an urgent need to normalise AI transparency as a core component of open research practice.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":" ","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex and gender reporting in Australian cancer clinical trials: are we doing enough?","authors":"Vikneswary Batumalai, Gowri Shivasabesan, Cheryl Carcel, Carole A Harris, Sue Haupt, Eng-Siew Koh, Angela Liao, Shalini K Vinod, Mei Ling Yap","doi":"10.1186/s41073-026-00218-x","DOIUrl":"10.1186/s41073-026-00218-x","url":null,"abstract":"<p><strong>Background: </strong>Sex and gender influence cancer incidence, treatment response, and outcomes, yet reporting of these variables remains inconsistent in clinical research. Australia represents a distinct research governance context, where sex and gender considerations are recommended but not systematically embedded in regulatory oversight, and where no national evaluations of reporting in cancer trials exist. By examining Australian cancer randomised controlled trials (RCTs) published from 2014-2024, this study addresses a critical evidence gap and extends prior international assessments by providing the first national-level analysis of reporting practices in relation to the Sex and Gender Equity in Research (SAGER) guidelines.</p><p><strong>Methods: </strong>A review of RCTs conducted in Australia or led by Australian investigators and published between January 2014 and October 2024 was undertaken. Studies were assessed for adherence to key SAGER criteria, including correct use of terminology, reporting of sex/gender in abstracts and tables, consideration in study design, inclusion of sex/gender-based analyses, and discussion of implications. For temporal comparisons, the introduction of the SAGER guidelines in 2016 was used as the cut‑off, with trials grouped as pre‑SAGER (2014-2016) and post‑SAGER (2017-2024).</p><p><strong>Results: </strong>Of 128 eligible studies, none reported how sex or gender was defined. Overall, 50% used appropriate terminology, 14% reported sex/gender in abstracts, 16% considered these variables in design, 28% conducted sex/gender-based analyses, and 11% discussed implications. Post-SAGER, there was an increase in appropriate terminology use (from 39% to 54%), sex/gender breakdown in abstracts (from 9% to 16%), and demographic tables with separate rows for males and females (from 41% to 69%). However, these tables typically provided only overall sex counts; only 10% of studies presented a complete sex/gender breakdown across all baseline characteristics. References to sex/gender relevance in introductions declined (12% to 2%), consideration in study design decreased (21% to 14%), and sex/gender-based analyses dropped (44% to 22%).</p><p><strong>Conclusion: </strong>Sex and gender reporting in Australian cancer RCTs remains inconsistent and suboptimal. While modest gains were achieved post-SAGER, stronger editorial oversight, funding mandates, and researcher education are needed to ensure equitable, rigorous, and generalisable cancer research.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13343558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction and correction of scientific articles due to conflicts of interest: a cross‑sectional analysis of the Retraction Watch database.","authors":"Ružica Bočina, Antonija Mijatović, Ana Marušić","doi":"10.1186/s41073-026-00236-9","DOIUrl":"10.1186/s41073-026-00236-9","url":null,"abstract":"<p><strong>Background: </strong>Conflicts of interest (CoI) can undermine trust in scientific publishing, yet empirical evidence on how often CoI lead to editorial action and how journals respond remains limited. This study aimed to characterise the frequency, patterns, and editorial handling of CoI‑related notices in the scholarly literature using an openly available, multidisciplinary dataset.</p><p><strong>Methods: </strong>We conducted a descriptive cross‑sectional analysis of publicly available records from the Retraction Watch (RW) database, accessed via Crossref, from inception to 23 April 2026. All editorial notices in which CoI was explicitly mentioned as the sole or one of several stated reasons were included, encompassing retractions, corrections, expressions of concern, and reinstatements. For each record, we extracted article and notice dates, article type, scientific field, country of the corresponding author, number of authors and institutions, publisher, and all stated reasons for the notice. Data were analysed using descriptive statistics and bootstrap‑based confidence intervals.</p><p><strong>Results: </strong>We identified 886 CoI‑related records (748 retractions, 39 corrections, 97 expressions of concern, and 2 reinstatements). CoI was the sole stated reason in 95 cases (10.7%), including 32 retractions, 11 corrections, and 52 expressions of concern; in the remaining notices, CoI most commonly co‑occurred with concerns such as compromised peer review, authorship or affiliation problems, journal or institutional investigations, and broader ethical violations. The median time from publication to editorial action was longest for expressions of concern and shortest for corrections, with wide variability across records and notice types. CoI‑related notices were concentrated in basic life, biomedical, and health sciences, and were unevenly distributed across high‑output countries and a small number of large publishers, particularly open‑access outlets. Most affected articles were original research papers with multi‑author, multi‑institution teams. Among articles with determinable author gender, both first and last authors were more often male than female, although these patterns were exploratory and lacked a baseline comparator.</p><p><strong>Conclusions: </strong>Editorial actions explicitly linked to CoI remain relatively uncommon compared with the broader volume of retractions and corrections, and CoI are rarely cited in isolation. The frequent co‑occurrence of CoI with other forms of misconduct or procedural problems suggests that undisclosed or poorly managed CoI often signal deeper weaknesses in disclosure practices, editorial oversight, and research governance rather than isolated administrative lapses. Strengthening CoI policies, transparency, verification mechanisms, and linked metadata systems may help detect problems earlier and support more consistent, graduated editorial responses.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":" ","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prioritising measures and interventions to strengthen research reproducibility: a Delphi consultation study.","authors":"Dora Pejdo, Ivan Buljan, Ana Marušić","doi":"10.1186/s41073-026-00217-y","DOIUrl":"10.1186/s41073-026-00217-y","url":null,"abstract":"<p><strong>Background: </strong>Research reproducibility - the ability of others to independently verify scientific findings by following the same methods and data - is a fundamental aspect of research integrity. When this fails, trust in evidence is undermined and research resources are wasted. Reproducibility measures describe the standards that define reproducible research, and reproducibility interventions are the actions used to improve those standards. There is little agreement on which practices and initiatives should be prioritised for implementation in practice. This study aimed to establish expert consensus on the key measures and interventions that should be prioritised to strengthen research reproducibility.</p><p><strong>Methods: </strong>We conducted a Delphi consensus study as part of the EU Horizon Europe iRISE project. Experts from five stakeholder groups (researchers, editors, publishers, funders, and policymakers) evaluated reproducibility measures and interventions identified through prior literature mapping across two online survey rounds, followed by a final virtual consensus panel. Items were rated on a 10-point Likert scale, with consensus defined a priori as agreement by at least 70% of panellists assigning high-priority scores, corresponding to scores of eight to ten on the Likert scale.</p><p><strong>Results: </strong>Seventy-three panellists from 34 countries participated in the first round, with high retention rates in the subsequent round. Consensus was achieved on eight reproducibility measures and six reproducibility interventions in the first round. Prioritised measures included methodological quality, reporting quality, code and data availability and reuse, computational reproducibility, transparency of research plan, reproducible workflow practices, trial registration, and materials availability and reuse. Prioritised interventions included data management training, data quality checks/feedback, statistical training, data sharing policy/guidelines, protocol/trial registration, and reproducible code/analysis training.</p><p><strong>Conclusions: </strong>The prioritised measures and interventions provide a structured foundation for improving research reproducibility across disciplines. The findings can inform the development of institutional training curricula in data management and statistical methods, policies supporting data sharing and trial registration, and future empirical evaluation of these practices across research contexts.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13330243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What influences whether researchers adhere to healthcare reporting guidelines successfully? A thematic synthesis.","authors":"James Harwood, Charlotte Albury, Jennifer de Beyer, Zhaoxiang Bian, Yuting Duan, Shona Kirtley, Michael Schlüssel, Lingyun Zhao, Gary S Collins","doi":"10.1186/s41073-026-00209-y","DOIUrl":"10.1186/s41073-026-00209-y","url":null,"abstract":"<p><strong>Background: </strong>Despite endorsement by medical journals, reporting guidelines have only modestly affected reporting quality in healthcare research. We aimed to identify influences affecting whether authors successfully adhere to reporting guidelines.</p><p><strong>Methods: </strong>We searched MEDLINE, Embase, PsychINFO, AMED, WHO Global Index Medicus, SciELO, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data, VIP Chinese Medical Journal Database, OSF, and MiRoR for qualitative research exploring researchers' experiences of reporting guidelines for healthcare research, published after 1996 in English, Chinese, Spanish, or Portuguese. We appraised studies using CASP-Qual. For thematic synthesis, we applied descriptive codes to all text reporting qualitative findings, then aggregated codes inductively into descriptive themes that captured the codes' meaning. We interpreted and contextualised possible influences from these descriptive themes to create analytic themes.</p><p><strong>Results: </strong>From 18 eligible studies, we developed 12 analytic themes: 1) Researchers may not understand guidance as intended or what reporting guidelines are, even if they think they do; 2) Researchers report a variety of reasons for using reporting guidelines, and some are more important than others; 3) Researchers describe using reporting guidelines for different tasks and wanting guidance delivered in ways that better fit their needs; 4) Using reporting guidelines has costs which researchers may feel outweigh benefits; 5) Reporting guidelines may need to be revised and updated; 6) Researchers may not be able to report all items, which can leave them feeling uncertain or worried; 7) Awareness and accessibility may limit reporting guideline usage; 8) Reporting guidelines may be more useful to less experienced researchers, but these researchers may find them harder to use; 9) Researchers want or need design advice, but reporting guidelines may not be the right place to find it; 10) Reporting guidelines can be harder to use if their scope is too broad, too narrow, or poorly defined; 11) Researchers may have to use multiple sets of reporting guidelines, multiplying complexity and costs; 12) Researchers may use checklists but never read the full guidance.</p><p><strong>Discussion: </strong>We identified many influences despite a paucity of evidence. Addressing these influences when developing, refining, and implementing reporting guidelines may improve adherence.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13326295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential and limitations of a large language model in the statistical review of comparative categorical data: an exploratory study of structured prompt-guided approach.","authors":"Min Dong, Xuemei Liu, Yunmei Luo, Yuanxiang Zeng, Fang Fei, Liang Du","doi":"10.1186/s41073-026-00231-0","DOIUrl":"10.1186/s41073-026-00231-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Statistical review is essential for research quality and integrity, yet traditional manual review is inefficient. Large language models (LLMs) offer potential support but are unreliable when used without guidance for precise calculations and raise concerns about accountability. This study evaluated whether a structured, rule-based prompt can reliably constrain an LLM to perform statistical review of comparative categorical data, and characterized both its feasibility and its inherent risks from an accountability perspective.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study employed a two-stage design based on the DeepSeekV3.2. In the first stage, a structured prompt was developed through dozens of \"test-fail-iterate\" cycles using 20 published medical articles. The prompt assigned the LLM the role of a \"statistics expert\" and provided a closed set of computational rules and a \"recognize data-select calculation formula-calculate\" workflow for analyzing categorical data, including Pearson's Chi-square test, continuity correction, and McNemar's tests. In the second stage, the performance of the final prompt was evaluated on a test set of 20 independent manuscripts. The model's output was compared against the results calculated by a senior statistician (the gold standard). The primary outcome measures were the performance in statistical method selection and numerical computation, including accuracy, sensitivity (recall), specificity, positive predictive value, negative predictive value, F1 score, and Cohen's Kappa. Secondary measures included reproducibility and efficiency.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The test set consisted of 15 manuscripts with independent samples and 5 with paired samples. In the assessment of the appropriateness of statistical method selection for 148 analysis items, the model achieved an accuracy of 99.3% (147/148), a sensitivity of 96.2% (25/26) (F1=98.0%, κ=0.976). For the test of computational consistency in 97 independent sample tests, the accuracy for χ&lt;sup&gt;2&lt;/sup&gt; value consistency was 94.8% (92/97) (F1=89.3%, κ=0.859), and for P-value consistency, it was 96.9% (94/97) (F1=90.9%, κ=0.891). In the paired-sample analysis, the model's methods and results were in perfect agreement with the manual review, and prompt optimization eliminated discrepancies in degrees-of-freedom calculation rules. Efficiency analysis showed no statistically significant difference in time consumption between the model (407 s) and manual review (374 s) (P=0.601). In reproducibility tests, the intraclass correlation coefficients for both χ&lt;sup&gt;2&lt;/sup&gt; values and P-values exceeded 0.91. However, qualitative analysis revealed 3 typical failure modes in the task workflow: (1) Instability: The model's failure to produce identical outputs across repeated runs, manifesting as inconsistent data extraction or the failure to process all designated tasks (scope neglect). (2) Performance degradation/\"lazy\" behavior: A decline in exec","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":" ","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publication integrity and industry influences on the editorial process: a case study.","authors":"Evan D Kharasch, Michael M Todd, Jean-Francois Pittet, William L Lanier","doi":"10.1186/s41073-026-00216-z","DOIUrl":"10.1186/s41073-026-00216-z","url":null,"abstract":"<p><p>Scholarly publishing is facing unprecedented challenges. Journal scholarly integrity and ethics have historically focused on author conduct and conflicts of interest. We address apparent journal and editor conflicts of interest. We focus on a series of events attending the publication in a peer-reviewed biomedical journal of a research article and four contemporaneously published editorials. A time series of events and questions which they engender follows the events description. We dissect and question behaviors and motivations, and accompanying transparency and accountability, and how these can affect article and journal credibility. The need for journals and scholarly publishing to recognize their own conflicts is discussed, as is the need for the science publishing to be self-correcting, in addition to science articles themselves. Author conflicts of interest and the need for attendant disclosure have become part of the publication process. Journal conflicts can be no less impactful and important, and merit the same disclosure, transparency, and rigor, in order to protect the public trust in biomedical science and publishing. When issues are encountered, science publishing needs to be self-correcting.</p>","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13321656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior characteristics of peer reviewers in medical journals: a survey from China.","authors":"Guie Liu, Yuan Tian, Jing Peng, Lianyang Zhang, Lei Li","doi":"10.1186/s41073-026-00215-0","DOIUrl":"10.1186/s41073-026-00215-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Peer review is fundamental to quality scientific communication, yet reviewer behavior remain underexplored. The impact of emerging large language models (LLMs) on peer review practices is similarly understudied. We aim to characterize behavioral traits of Chinese medical journal peer reviewers and identify evidence-based recommendations to optimize review willingness, efficiency and quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An online questionnaire survey was distributed to 532 medical researchers in China through the Wenjuanxing platform in February 2025. The questionnaire (38 questions) assessed four domains: basic information, peer review model and efficiency, peer review quality, and reviewer motivations. Statistical analysis included descriptive statistics, Spearman correlations, Kruskal-Wallis tests, etc. RESULTS: The response rate was 51.9% (276/532, 95% confidence interval (CI): 47.6%-56.1%). The valid questionnaires were 275: 91.6% male; 64.7% of 41-55 years old. Double-blind was supported by 80.7% of respondents, exceeding international prevalence. Reviewers exhibited social desirability bias in self-reported review turnaround time: 92.7% reported completing reviews within 15 d, whereas the actual recent 3-year administrative data was only 69.7% (P &lt; 0.001, Cramér's V = 0.303). Reviewers expected their submissions to finish review in 15 d and at most 60 d, which was very pressurized for the editorial office. Efficient reviewers expected their manuscripts to be reviewed faster (ρ = 0.551, 95% CI: 0.460-0.630). Reviewers weighted scientificity and novelty most heavily (30% each), followed by clinical feasibility (20%). Review quality showed heterogeneity: 17.8% of respondents (49/275) reported &lt; 50% agreement with feedback received on their submissions vs. 47.6% (131/275) reporting ≥ 70% agreement. Only 24.7% respondents used LLM for peer review assistance, yet 91.2% of users reported a positive impact. The most frequently used LLMs in China were DeepSeek, Doubao, ChatGPT and Kimi in sequence. Compared with males, female reviewers were more likely to use LLM for peer review assistance (43.5% vs. 23.0%, P = 0.029, Cohen's h = 0.439), but the difference was only significant in DeepSeek (P = 0.005). LLM use did not significantly alter main peer review characteristics (all P &gt; 0.05). Regarding motivation, recognition and acknowledgment ranked first (74.9%), followed uniquely in China by requests for priority handling of their submissions (64.4%) and recommended submissions (63.6%), reflecting publication-pressure contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Misalignments exist between reviewer expectations and editorial capacity regarding review efficiency. System-level improvements in manuscript handling system and implementation of standardized review templates and training may improve review quality. Formal recognition of review contributions and fast handling of reviewer's submissions could enhance motivation","PeriodicalId":74682,"journal":{"name":"Research integrity and peer review","volume":"11 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13321697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}