Impact of the COVID-19 Pandemic on Academic Productivity in Oncology: A Journal-, Conference- and Author-Level Analysis
Bibliographic record
Abstract
This study assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on academic productivity in oncology, measured by conference abstracts, journal publications and individual authorship trends, using a reference time frame of 2018 to 2022. To assess overall academic productivity, data was obtained on the number of abstracts and articles submitted and published from a selection of oncology conferences and journals. To assess individual authorship patterns, 200 articles were randomly selected from 2018, and for the first or last authors, publications were tracked over subsequent years. Factors assessed included gender, continent, specialty, MD vs. non-MD and career status (early vs. late). The number of submitted and published conference abstracts trended downward over time between 2018 and 2022 (p=0.11 and p=0.16 respectively). Journal submissions increased to a peak in 2020 and then declined thereafter, but this did not translate into changes in the number of papers published. For the author-level analysis, factors significantly predictive of increasing publication rates in multivariable analysis were late career status (vs. early), clinician status (vs. non-clinician), surgery or public health/epidemiology specialty, and author located in Asia. Further research is needed to help ameliorate the impact of these disparities.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".