Copyright License

Open licensing for reusable health statistics research.

IJHS publishes open access content to support broad reuse with proper attribution. Authors retain copyright while enabling sharing and adaptation.

The license supports educational, clinical, and policy use of statistical evidence.

Open licensing enables reuse in teaching, clinical training, and policy development with proper attribution.

Notify the editorial office if your funder requires specific license language.

Translations should include the original publication details and licensing terms.

Well structured manuscripts accelerate peer review and help readers apply statistical insights to real world health decisions.

Report software versions and packages to support reproducibility across analytic environments.

Describe any model tuning or hyperparameter selection to support reproducibility in machine learning workflows.

Authors may reuse their work in institutional repositories, teaching materials, and future publications with appropriate citation.

Authors retain copyright and may archive accepted manuscripts in institutional repositories.

Reuse is permitted for educational and noncommercial training with proper citation.

Reuse should not imply endorsement; maintain context in derivative works.

Provide uncertainty measures such as confidence intervals or credible intervals for key estimates and model outputs.

When combining datasets, document linkage procedures and quality checks for matching accuracy.

If data access is restricted, describe the approval process for qualified researchers and expected timelines.

If third party content is used, obtain permissions and clearly label the source in captions and acknowledgments.

Authors may share the accepted manuscript on personal or institutional webpages.

Include license notice in supplementary materials when shared separately.

Explain how missing data were handled and why chosen strategies were appropriate for the study design.

Highlight ethical safeguards for patient privacy, especially when working with linked or sensitive datasets.

For time series analyses, describe seasonality handling and any interventions or policy changes considered.

If third party figures or datasets are included, obtain permission and identify sources clearly.

Material not covered by the open license should be labeled in captions or acknowledgments.

Proper citation of the DOI and journal title is required for reuse or adaptation.

License terms should be reflected in any derivative products or translations.

Clear statistical reporting improves the interpretability of health evidence for clinicians, policymakers, and research funders.

When presenting predictive models, report calibration, discrimination, and decision curve metrics where relevant.

Include brief rationale for study design choices to support reviewer understanding and methodological transparency.

When reporting health disparities, describe how social determinants and contextual factors are measured.

Reuse must include citation of the article title, DOI, and journal name.

• Credit the original authors
• Link to the DOI and journal page
• Indicate any modifications

When adapting figures or tables, describe modifications and retain attribution to the original source.

If you adapt content, note the changes to maintain transparency.

We encourage authors to document assumptions and sensitivity analyses so conclusions remain robust across populations.

Define statistical terminology clearly for multidisciplinary readers who apply methods in clinical settings.

Use tables and figures to communicate effect sizes, uncertainty, and subgroup comparisons clearly.

Include data dictionary summaries or variable definitions for key covariates to improve interpretability.

Reuse should preserve scientific context to avoid misinterpretation in secondary materials.

Derivative works should retain the original citation and DOI link.

Transparent reporting of data provenance and governance supports reproducibility and ethical compliance in health statistics.

Summaries that connect statistical findings to health outcomes improve translation to policy and practice.

If external validation is performed, describe population differences and implications for generalizability.