COVID-19 and consent for research: Navigating during a global pandemic
Bibliographic record
Abstract
The modern ethical framework demands informed consent for research participation that includes disclosure of material information, as well as alternatives. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) results in illness that often involves rapid deterioration. Despite the urgent need to find therapy, obtaining informed consent for COVID-19 research is needed. The current pandemic presents three types of challenges for investigators faced with obtaining informed consent for research participation: (1) uncertainty over key information to informed consent, (2) time and pressure constraints, and (3) obligations regarding disclosure of new alternative therapies and re-consent. To mitigate consenting challenges, primary investigators need to work together to jointly promote urgent care and research into COVID-19. Actions they can take include (1) prior plan addressing ways to incorporate clinical research into clinical practice in emergency, (2) consider patients vulnerable with early deliberation on the consent process, (3) seek Legally Authorized Representatives (LARs), (4) create a collaborative research teams, (5) aim to consent once, despite evolving information during the pandemic, and (6) aim to match patients to a trial that will most benefit them. The COVID-19 pandemic both exacerbates existing challenges and raises unique obstacles for consent that require forethought and mindfulness to overcome. While research teams and clinician-investigators will need to be sensitive to their own contexts and adapt solutions accordingly, they can meet the challenge of obtaining genuinely informed consent during the current pandemic.
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 | 0.413 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.107 |
| Scholarly communication | 0.036 | 0.058 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.038 | 0.071 |
| Insufficient payload (model declined to judge) | 0.009 | 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".