Bibliometric Analysis of COVID-19 in the Context of Migration Health: A Study Protocol
Bibliographic record
Abstract
ABSTRACT Introduction Human mobility has been pivotal to the spread of COVID-19 through travel and migration. To mitigate the spread, most countries have imposed strict travel restrictions that have severely affected both the wellbeing and livelihoods of many migrant and mobile populations (both internally and internationally), particularly those from impoverished communities, those affected by humanitarian crises, including populations displaced and/or living in camps and camp-like settings. The need to include migrants (both regular and irregular or ‘undocumented’) in national strategic response plans for disease prevention and control has been increasingly recognized. Better understanding of the existing scientific evidence in migration health is crucial in designing effective response measures. In this paper, we present a protocol for a bibliometric analysis of scientific publications on COVID-19 and migration health. Expected study findings aim to provide valuable information to support evidence mapping on COVID-19 and migration health, particularly the identification of important research gaps. Methods and analysis Using Elsevier’s Scopus abstract and citation database, a comprehensive search strategy will be applied to map scientific publications on COVID-19 and migration health. The current analysis will focus on research published from 1 January 2020 to 4 May 2020. The search query on migration health will largely focus on migration, migrant and human mobility-related terms. Three reviewers will screen publications for eligibility. The extracted bibliographic information will be analysed to determine the dominant research themes, country coverage and migrant groups. Collaboration networks will be analysed using VosViewer, a network analysis software. A deep dive on dominant research themes or migrant health-related topics will be done by creating visualization network maps of keywords from the retrieved publications. Ethics and dissemination This analysis will draw on publicly available data and does not directly involve human participants; ethics review is not required.
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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 | 0.113 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.044 | 0.043 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".