Improving biobank consent comprehension: a national randomized survey to assess the effect of a simplified form and review/retest intervention
Bibliographic record
Abstract
PURPOSE: To determine the individual and combined effects of a simplified form and a review/retest intervention on biobanking consent comprehension. METHODS: We conducted a national online survey in which participants were randomized within four educational strata to review a simplified or traditional consent form. Participants then completed a comprehension quiz; for each item answered incorrectly, they reviewed the corresponding consent form section and answered another quiz item on that topic. RESULTS: Consistent with our first hypothesis, comprehension among those who received the simplified form was not inferior to that among those who received the traditional form. Contrary to expectations, receipt of the simplified form did not result in significantly better comprehension compared with the traditional form among those in the lowest educational group. The review/retest procedure significantly improved quiz scores in every combination of consent form and education level. Although improved, comprehension remained a challenge in the lowest-education group. Higher quiz scores were significantly associated with willingness to participate. CONCLUSION: Ensuring consent comprehension remains a challenge, but simplified forms have virtues independent of their impact on understanding. A review/retest intervention may have a significant effect, but assessing comprehension raises complex questions about setting thresholds for understanding and consequences of not meeting them.Genet Med advance online publication 13 October 2016.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".