Exploring Canadian genetic healthcare providers’ perspectives on sponsored genetic testing
Bibliographic record
Abstract
Sponsored genetic testing (SGT) programs consist of partnerships between clinical genetic testing laboratories and third-party organizations (generally biopharmaceutical companies) to offer genetic testing free of charge to a patient or healthcare system. To date, there is no research surrounding the use of SGT in Canada, or how it is perceived by professionals. This study aims to learn about Canadian genetic healthcare providers’ (CGHPs’) views on SGT, along with their perceived benefits, limitations, and impacts of SGT within the Canadian healthcare system. Certified genetic counsellors, medical geneticists, and laboratory geneticists practicing in Canada were invited to participate in semi-structed interviews. Interviews were recorded over Zoom, transcribed verbatim, and analyzed using interpretive description and thematic analysis. Codes were created inductively, and themes emerged across cases to capture participants’ perceptions. Interviews were conducted with 18 CGHPs across six provinces. Some participants were ambivalent about SGT, and others either agreed or disagreed with its use in practice. Perspectives were categorized into four main themes: 1) adequate transparency surrounding data sharing 2) the desire for a workaround to improve access 3) consideration of budgets within a publicly funded healthcare system and 4) perspectives of non-genetics providers using SGT. Proponents noted that transparency regarding data sharing between the genetic testing laboratories and third-party companies was adequate, that SGT could provide increased access to genetic testing, and that SGT can help advocate for enhanced provincial funding of genetic services. Skeptics of SGT mentioned a lack of transparency regarding how patient data is shared and used, that a public system should be able to cover all patients who require genetic testing, and that there is a responsibility to consider how externally funded testing could be detrimental to future budget considerations. All participants had considerations for their non-genetics colleagues ordering SGT. This exploratory study offers insights surrounding CGHPs’ views on SGT. It highlights the benefits and limitations regarding the use of SGT in Canada, along with a unique perspective into the challenges and nuances of using SGT within a publicly funded healthcare system.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".