Development of a Multimedia Electronic Consent Platform for Biobanking and Research Utilizing Opinions from Children, Teens, and Adults
Bibliographic record
Abstract
OBJECTIVE: Ethical research recruitment relies on effective informed consent. We sought feedback and acceptance from users regarding an interactive, multimedia, electronic consent (e-consent) platform for recruitment of research participants to the BC Children's Hospital BioBank (BCCHB). We aimed to enhance user experience when considering research participation and documenting consent decisions through the modality of an e-consent. STUDY DESIGN: A prototype e-consent was developed and end-user opinions regarding content, visuals, user satisfaction, and electronic consent/assent practices were obtained from children, teens, and adults via an online survey and focus groups. A finalized e-consent was submitted for research ethics board (REB) approval. RESULTS: All age groups rated the description of information, images, and formatting in the e-consent as highly favorable. Teens and adults preferred online (38% and 42%) rather than paper-based (17% and 16%) consent, while children expressed no preference. Majority of children (100%), teens (92%), and adults (98%) agreed or strongly agreed that they understood all the information given during the online consent process. No significant differences were found in survey responses between age groups. Adult and teen focus groups suggested improvements in formatting and addition of features to further clarify terms like "ongoing donation" and "privacy measures." All ages preferred the ability to complete the e-consent independently, with optional assistance from research staff. The e-consent received REB approval and was implemented for BCCHB recruitment. CONCLUSION: An e-consent was developed and its modality was successfully accepted by end-users from several age groups, including children and teens, for use in pediatric biobanking. This method may potentially improve the process of completing research consent, particularly with adolescents.
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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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".