Exploring individuals’ experiences with self-reported unmet need for genetic testing
Bibliographic record
Abstract
In practice, healthcare systems and insurers determine that there is “need” for genetic testing when there is potential for clinical utility. However, it is not currently known how the public understand need for genetic testing and if this aligns with clinical utility. We recruited participants in Canada through a survey distributed through a market research company (Leger Opinion Panel). Participants who self-reported need for genetic testing were then purposively sampled to complete a semi-structured virtual interview. We used an interpretive description approach and reflexive thematic analysis. We completed 19 interviews and found that participants’ self-identified need for genetic testing was informed by their experiences with genetic information, and the perceptions that genetic information is actionable (clinical utility) and has personal meaning (personal utility). Most participants would not be eligible for funded testing based on their personal and family history, however they had unmet informational and psychological needs, indicating unmet need for genetic counseling. The public understanding of need for genetic testing is complex and varied. Participants identified many benefits resulting from genetic testing which are not reflected in how need is operationalized in reimbursement decisions, however unmet expectations for testing contributed to medical distrust and dissatisfaction.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".