Verbal consent in biomedical research: moving toward a future standard practice?
Bibliographic record
Abstract
Properly obtaining informed consent is a core obligation for research conducted using human subjects. The traditional informed consent process involves written forms and obtaining signatures. This process remains the standard, but in various research settings, such as COVID-19 and rare disease research, verbal consent has increasingly become the norm. Although verbal consent is used in these settings, its use is still a subject of debate. This article reviews in what medical settings verbal consent is commonly seen today, various advantages and disadvantages of verbal consent, and its legislative and policy ecosystem. In doing so, this review article asserts that it is time for the debate over verbal consent to come to an end and for legislator and policymakers to acknowledge its use and to formalize the process. This will allow verbal consent to be regulated in a similar manner to written consent and will give clinician-researchers guidance on how to better implement verbal consent in their studies to addressing ongoing concerns with the consenting process as a whole.
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 | 0.332 | 0.387 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.021 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".