Impact of the COVID-19 Pandemic on Cardiovascular Science: Anticipating Problems and Potential Solutions: A Presidential Advisory From the American Heart Association
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had worldwide repercussions for health care and research. In spring 2020, most non-COVID-19 research was halted, hindering research across the spectrum from laboratory-based experimental science to clinical research. Through the second half of 2020 and the first half of 2021, biomedical research, including cardiovascular science, only gradually restarted, with many restrictions on onsite activities, limited clinical research participation, and the challenges associated with working from home and caregiver responsibilities. Compounding these impediments, much of the global biomedical research infrastructure was redirected toward vaccine testing and deployment. This redirection of supply chains, personnel, and equipment has additionally hampered restoration of normal research activity. Transition to virtual interactions offset some of these limitations but did not adequately replace the need for scientific exchange and collaboration. Here, we outline key steps to reinvigorate biomedical research, including a call for increased support from the National Institutes of Health. We also call on academic institutions, publishers, reviewers, and supervisors to consider the impact of COVID-19 when assessing productivity, recognizing that the pandemic did not affect all equally. We identify trainees and junior investigators, especially those with caregiving roles, as most at risk of being lost from the biomedical workforce and identify steps to reduce the loss of these key investigators. Although the global pandemic highlighted the power of biomedical science to define, treat, and protect against threats to human health, significant investment in the biomedical workforce is required to maintain and promote well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.023 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".