Impact of the COVID-19 Pandemic on Cancer Researchers in 2020: A Qualitative Study of Events to Inform Mitigation Strategies
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has had an unprecedented impact on global health systems and economies. With ongoing and future challenges posed to the field due to the pandemic, re-examining research priorities has emerged as a concern. As part of a wider project aiming to examine research priorities, here we aimed to qualitatively examine the documented impacts of the COVID-19 pandemic on cancer researchers. Materials and Methods: We conducted a literature review with the aim of identifying non-peer-reviewed journalistic sources and institutional blog posts which qualitatively documented the effects of the COVID-19 pandemic on cancer researchers. We searched on 12th January 2021 using the LexisNexis database and Google, using terms and filters to identify English-language media reports and blogs, containing references to both COVID-19 and cancer research. The targeted search returned 751 results, of which 215 articles met the inclusion criteria. These 215 articles were subjected to a conventional qualitative content analysis, to document the impacts of the pandemic on the field of cancer research. Results: Our analysis yielded a high plurality of qualitatively documented impacts, from which seven categories of direct impacts emerged: (1) COVID measures halting cancer research activity entirely; (2) COVID measures limiting cancer research activity; (3) forced adaptation of research protocols; (4) impacts on cancer diagnosis, cases, and services; (5) availability of resources for cancer research; (6) disruption to the private sector; and (7) disruption to supply chains. Three categories of consequences from these impacts also emerged: (1) potential changes to future research practice; (2) delays to the progression of the field; and (3) potential new areas of research interest. Discussion: The COVID-19 pandemic had extensive practical and economic effects on the field of cancer research in 2020 that were highly plural in nature. Appraisal of cancer research strategies in a post-COVID world should acknowledge the potential for substantial limitations (such as on financial resources, limited access to patients for research, decreased patient access to cancer care, staffing issues, administrative delays, or supply chain issues), exacerbated cancer disparities, advances in digital health, and new areas of research related to the intersection of cancer and COVID-19.
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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 | 0.046 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".