Development and user testing of gene pilot: an electronic health decision support tool for Black cancer patients about tumor genomic profile testing
Bibliographic record
Abstract
Introduction Tumor genomic profiling (TGP) is used to optimize cancer treatment but is underutilized by Black patients, despite having disproportionately higher cancer morbidity and mortality. No interventions using electronic health decision support tools (eHealth DSTs) have been developed to assist patients with understanding this test or address barriers to uptake and communicating preferences with a doctor. Methods Using the Ottawa Decision Support Framework, we systematically developed the Gene Pilot eHealth DST with Black cancer patients. We conducted qualitative focus groups (five groups, N = 33) and surveys ( N = 121), elicited community advisory board feedback ( N = 10) to devise DST content and communication strategies, and then conducted user testing ( N = 10). Content was informed by commercial marketing techniques - segmentation, perceptual mapping, vector message modeling – to elucidate how medical mistrust was an important construct to address in Gene Pilot. Results User testing (1–7 scale) indicated Gene Pilot was highly accepted, including ease of use (M = 6.67, SD = 0.50), that it addressed important barriers such as medical mistrust and genetic literacy (6.63, SD = 0.52), and allowed patients to prepare for the decision (M = 6.44, SD = 0.73) and to communicate with their doctor (M = 6.33, SD = 0.73). Suggestions for improved navigability were addressed. Conclusion Overall, Gene Pilot was found to be acceptable, suggesting its readiness for efficacy testing.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".