Putting control into parents' hands: Parent experiences with a genomic results e‐booklet
Bibliographic record
Abstract
We evaluated the clinical use of a customizable, multi-language, Genomic Results Booklet (GRB)-a printable e-booklet co-designed with parents-to provide information and guidance to families post-genomic testing. The GRB provides individual genomic results, with implications and resources, all in family-friendly language. Participants were parents of children offered genomic testing in a pediatric neurology clinic. Two and eight weeks after GRB receipt, parents completed surveys to assess usage of the e-booklet. Parents then had a semi-structured telephone interview about their experiences, which were analyzed using interpretive description. Thirty-four parents received a customized GRB, including versions in Punjabi and Arabic. Seventeen booklets were for pathogenic test results, and the other 17 were for noninformative results. The surveys showed that all families would recommend the GRB and had used its resources or supports. About 80% shared it with others, and 67% described it as helpful in future planning. Analysis of 20 parent-interviews revealed that parents valued understandable, relevant information; a written e-pamphlet; a list of appropriate resources; and practical guidance. The GRB is valued by parents to explain their individual genomic testing results, to provide useful supports, specific resources, and a sense of direction in the weeks after receiving results.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".