Simple genetics language as source of miscommunication between genetics researchers and potential research participants in informed consent documents
Bibliographic record
Abstract
Informed consent is based on communication, requiring language to convey meanings and ensure understandings. The purpose of this study was to investigate the use of language in informed consent documents used in the genetics research funded by Canadian Institutes of Health Research and Genome Canada. Consent documents were requested from the principal investigators in a recent round of funding. A qualitative content analysis was performed, supported by NVivo7™. Potential barriers to informed consent were identified, including language that was vague and variable, words with both technical and common meanings, novel phrases without clear meaning, a lack of definitions, and common concepts that assume new definitions in genetics research. However, we noted that difficulties in comprehension were often obscured because the words used were generally simple and familiar. We conclude that language gaps between researcher and potential research participants may unintentionally impair comprehension and ultimately impair informed consent in genomics research.
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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 | 0.356 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.009 |
| 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; 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".