Expert consensus on a protocol for conducting bibliometric analysis of scientific articles on global migration health (GMH)
Bibliographic record
Abstract
BACKGROUND: Migration and health are key priorities in global health and essential for protecting and promoting the health of migrants. To better understand the existing evidence on migration health, it is critical to map the research publication activity and evidence on the health of migrants and mobile populations. This paper presents a search strategy protocol for a bibliometric analysis of scientific articles on global migration health (GMH), leveraging the expertise of a global network of researchers and academics. The protocol aims to facilitate the mapping of research and evidence on the health of international migrants and their families, including studies on human mobility across international borders. METHODS: A systematic search strategy using Scopus will be developed to map scientific articles on GMH. The search strategy will build upon a previous bibliometric study and will have two main search components: (1) 'international migrant population', covering specific movements across international borders, and (2) 'health'. The final search strategy will be implemented to determine the final set of articles to be screened for the bibliometric analysis. Title and abstract screening will exclude irrelevant articles and classify the relevant articles according to predefined themes and subthemes. A combination of the following approaches will be used in screening: applying full automation (ie, DistillerSR's machine learning tool) and/or semiautomation (ie, EndNote, MS Excel) tools, and manual screening. The relevant articles will be analysed using MS Excel, Biblioshiny and VOSviewer, which creates a visual mapping of the research publication activity around GMH. This protocol is developed in collaboration with academic researchers and policymakers from the Global South, and a network of migration health and research experts, with guidance from a bibliometrics expert. ETHICS AND DISSEMINATION: The protocol will use publicly available data and will not directly involve human participants; an ethics review will not be required. The findings from the bibliometric analysis (and other research that can potentially arise from the protocol) will be disseminated through academic publications, conferences and collaboration with relevant stakeholders to inform policies and interventions aimed at improving the health of international migrants and their families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.290 | 0.426 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.052 | 0.042 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.083 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".