August 20, 2026 — Manila hosted the Asia Pacific Association of Medical Editors (APAME) 2026 Conference and the 7th Philippine Association of Medical Journal Editors (PAMJE) Conference, bringing together medical editors, researchers, and health professionals to strengthen research communication, publication practice, and editorial standards.
Held at the Henry Sy Building of the University of the Philippines Manila, the event featured discussions and learning activities focused on research quality, publication ethics, medical editing, and the responsible communication of evidence.
As part of the program, Venus Oliva Cloma-Rosales, MD, MPH, ME, Founder and Managing Director of 101 Health Research, facilitated two workshops:
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- Publication Quality and Ethics in the AI Age: How to Comply with the Latest Publication Guidelines
- Basic Statistics for Non-statisticians
Strengthening Publication Quality and Ethics in the AI Age
The first workshop, “Publication Quality and Ethics in the AI Age: How to Comply with the Latest Publication Guidelines,” examined how publication compliance is evolving in response to increasingly complex research and publishing environments.
The workshop moved beyond the idea that compliance is simply a final checklist before manuscript submission. Instead, it presented compliance as a continuous process that makes the trustworthiness of research explicit.
Publication quality has traditionally been considered through three dimensions: substance, form, and style. Substance concerns whether the research question, study design, methods, analysis, and conclusions are scientifically sound. Form addresses the completeness and technical acceptability of the manuscript. Style ensures that the work follows the conventions of the target journal and discipline.
In the age of artificial intelligence, a fourth dimension has become increasingly visible and urgent: integrity.
Integrity has always been central to responsible research and publication. However, the growing use of artificial intelligence has brought questions of authorship, originality, accountability, confidentiality, reproducibility, and verification to the forefront.
The workshop examined the interconnected publication guideline ecosystem, including guidance from the International Committee of Medical Journal Editors (ICMJE), the World Association of Medical Editors (WAME), the Committee on Publication Ethics (COPE), the EQUATOR Network, domain-specific organizations, institutions, funders, and target journals.
Each source serves a different purpose. ICMJE addresses authorship, contributions, conflicts of interest, research conduct, data sharing, peer review, and responsible artificial intelligence use. WAME provides ethical and professional guidance for editors, authors, reviewers, and publishers. COPE supports fair processes for addressing publication ethics concerns. The EQUATOR Network helps researchers identify appropriate reporting guidelines for different study designs.
Institutional and funder policies establish local expectations for ethics, privacy, data governance, security, and artificial intelligence use, while journal instructions provide the immediate requirements for manuscript preparation and submission.
Artificial intelligence was also discussed as an emerging publication compliance issue. AI tools cannot be authors because they cannot assume responsibility for the accuracy, integrity, originality, confidentiality, or final approval of a manuscript. Human authors remain accountable for all AI-assisted content, including claims, references, analyses, tables, code, images, and interpretations.
Responsible AI use requires transparency, human verification, protection of confidential information, careful checking of references, and documentation of significant decisions.
Participants were encouraged to develop a practical compliance system that begins during study planning and continues throughout the research and publication process. This may include creating a compliance map, documenting research and AI-related decisions, preserving source data and analysis files, clarifying authorship and contributions, disclosing conflicts and funding, and conducting a final review focused on transparency, traceability, security, and accountability.
Building Statistical Foundations for Health Research
The second workshop, “Basic Statistics for Non-statisticians,” provided participants with a practical foundation for understanding, selecting, and communicating statistical methods in health research.
The session began with scales of measurement, emphasizing that the type of data determines which summaries, statistical tests, and interpretations are appropriate. Participants reviewed nominal, ordinal, interval, and ratio data, and considered how variables should be classified before analysis begins.
The workshop then introduced two broad areas of statistical analysis:
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- Descriptive statistics, which summarize and present the characteristics of the data through frequencies, proportions, measures of central tendency, measures of variability, tables, and figures
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- Inferential statistics, which use sample data to estimate, compare, test, or draw conclusions about a wider population
The session also introduced diagnostic and model-checking concepts, including the importance of assessing whether statistical assumptions are met and whether the chosen analysis is appropriate for the research question and type of data.
A key emphasis throughout the workshop was that statistics should not be selected solely on the basis of familiarity or convention. The research question, study design, outcome variable, distribution of the data, and assumptions of the method should guide statistical decision-making.
Participants were also introduced to the SAMPL Guidelines, which provide recommendations for reporting statistical methods and results in medical and health research publications. These include clearly describing statistical methods, reporting estimates with appropriate measures of uncertainty, presenting exact P values when suitable, and ensuring that statistical results are interpreted in relation to the research question.
The workshop reinforced that good statistical practice involves more than obtaining a statistically significant result. It requires appropriate planning, transparent analysis, clear reporting, and responsible interpretation.
Together, the two workshops emphasized that high-quality research depends on both sound analysis and responsible communication.
The goal is not simply to produce a manuscript that passes through submission. It is to generate evidence that is appropriately analyzed, transparently reported, ethically conducted, and worthy of trust.
