Implementation of Electronic Informed Consent in Biomedical Research and Stakeholders’ Perspectives: Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Informed consent is one of the key elements in biomedical research. The introduction of electronic informed consent can be a way to overcome many challenges related to paper-based informed consent; however, its novel opportunities remain largely unfulfilled due to several barriers. OBJECTIVE We aimed to provide an overview of the ethical, legal, regulatory, and user interface perspectives of multiple stakeholder groups in order to assist responsible implementation of electronic informed consent in biomedical research. METHODS We conducted a systematic literature search using Web of Science (Core collection), PubMed, EMBASE, ACM Digital Library, and PsycARTICLES. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used for reporting this work. We included empirical full-text studies focusing on the concept of electronic informed consent in biomedical research covering the ethical, legal, regulatory, and user interface domains. Studies written in English and published from January 2010 onward were selected. We explored perspectives of different stakeholder groups, in particular researchers, research participants, health authorities, and ethics committees. We critically appraised literature included in the systematic review using the Newcastle-Ottawa scale for cohort and cross-sectional studies, Critical Appraisal Skills Programme for qualitative studies, Mixed Methods Appraisal Tool for mixed methods studies, and Jadad tool for randomized controlled trials. RESULTS A total of 40 studies met our inclusion criteria. Overall, the studies were heterogeneous in the type of study design, population, intervention, research context, and the tools used. Most of the studies’ populations were research participants (ie, patients and healthy volunteers). The majority of studies addressed barriers to achieving adequate understanding when using electronic informed consent. Concerns shared by multiple stakeholder groups were related to the security and legal validity of an electronic informed consent platform and usability for specific groups of research participants. CONCLUSIONS Electronic informed consent has the potential to improve the informed consent process in biomedical research compared to the current paper-based consent. The ethical, legal, regulatory, and user interface perspectives outlined in this review might serve to enhance the future implementation of electronic informed consent. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42020158979; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=158979
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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 | 0.193 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".