The impact of COVID-19 on global health journals: an analysis of impact factor and publication trends
Bibliographic record
Abstract
BACKGROUND: COVID-19 has affected research productivity across all areas of knowledge. Current evidence suggests that COVID-19 has had a blockbuster effect on journal impact factors (JIFs) and publication trends, while little is known on global health journals. METHODS: Twenty global health journals were included to analyse the impact of COVID-19 on their JIFs and publication trends. Indicator data, including numbers of publications, citations, articles with different types, etc, were extracted from journal websites and Web of Science Core Collection database. The JIFs from 2019 to 2021 were simulated for longitudinal and cross-sectional analyses. Interrupted time-series analysis and non-parametric tests were applied to assess whether COVID-19 had decreased non-COVID-19 publications from January 2018 to June 2022. RESULTS: In 2020, 615 out of 3223 publications were COVID-19 related, accounting for 19.08%. The simulated JIFs of 17 out of 20 journals in 2021 were higher than those in 2019 and 2020. Notably, 18 out of 20 journals had a decrease in their simulated JIFs after excluding COVID-19-related publications. Moreover, 10 out of 20 journals decreased their monthly numbers of non-COVID-19 publications after the COVID-19 outbreak. For all the 20 journals as a whole, after the COVID-19 outbreak in February 2020, the total number of non-COVID-19 publications significantly decreased by 14.2 compared with the previous month (p=0.013), and since then, on average, the publications had decreased by 0.6 per month until June 2022 (p<0.001). CONCLUSIONS: COVID-19 has impacted the structure of COVID-19-related publications, the JIFs of global health journals and their numbers of non-COVID-19 publications. Although journals may benefit from increased JIFs, global health journals should avoid relying on a single metric. More follow-up studies including more years of data with a combination of metrics should be conducted to generate more robust evidence.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".