Do Digital Tools Support a Diversity of Patient Values? A Qualitative Study using the Genetics Adviser
Bibliographic record
Abstract
Aim: To explore the range of values raised when selecting incidental findings (IF) from whole exome sequencing (WES) and how these are supported by the Genetics Adviser (GA).Methods: Qualitative interpretive descriptive study using semi-structured interviews conducted with participants undergoing WES and receiving IFs as part of the GARCT. Results: Sixteen participants were interviewed. Participants expressed that their decision was predetermined and occurred within a ‘black box’ before using the GA. Three values emerged from unpacking this ‘black box’: family stewardship, information imperative, and preserving agency. While the GA did not play an active role in eliciting these values, participants valued having the tool confirm their decision by clarifying, contextualizing, and instilling confidence in it. Conclusions: Participants entered decision-making with a predetermined decision guided by three core values. The GA supported them by reinforcing it. These findings can inform how digital tools can better support a diversity of patient values.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".