Understanding preferences for disclosure of individual biomarker results among participants in a longitudinal birth cohort
Bibliographic record
Abstract
BACKGROUND: To describe the preferences for disclosure of individual biomarker results among mothers participating in a longitudinal birth cohort. METHODS: We surveyed 343 mothers that participated in the Health Outcomes and Measures of the Environment Study about their biomarker disclosure preferences. Participants were told that the study was measuring pesticide metabolites in their biological specimens, and that the health effects of these low levels of exposure are unknown. Participants were asked whether they wanted to receive their results and their child's results. In addition, they were asked about their preferred method (letter vs in person) and format (more complex vs less complex) for disclosure of results. RESULTS: Almost all of the study participants wanted to receive their individual results (340/343) as well as their child's results (342/343). However, preferences for receiving results differed by education level. Mothers with less than a college degree preferred in-person disclosure of results more often than mothers with some college education or a college degree (34.3% vs 17.4% vs 7.9%, p<0.001). Similarly, mothers with less than a college education preferred a less complex disclosure format than mothers with some college education or a college degree (59.7% vs 79.1% vs 86.3%, p<0.0001). CONCLUSION: While almost all study participants preferred to receive results of their individual biomarker tests, level of education was a key factor in predicting preferences for disclosure of biomarker results. To ensure effective communication of this information, disclosure of biomarker results should be tailored to the education level of the study participants.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".