Canadian genetic healthcare professionals’ attitudes towards discussing private pay options with patients
Bibliographic record
Abstract
BACKGROUND: Just as there is inconsistency with respect to coverage of genomic testing with insurance carriers, there is interprovincial discrepancy in Canada. Consequently, the option of private pay (e.g., self pay) arises, which can lead to inequities in access, particularly when patients may not be aware of this option. There are currently no published data regarding how the Canadian genetics community handles discussions of private pay options with patients. The purpose of this study was to assess the attitudes of genetic healthcare professionals (GHPs: medical geneticists, genetic counselors, and genetic nurses) practicing in Canada toward these discussions. METHODS: An online survey was distributed to members of the Canadian College of Medical Geneticists and the Canadian Association of Genetic Counsellors to assess frequencies, rationale, and ethical considerations regarding these conversations. Quantitative data were analyzed using descriptive statistics. RESULTS: Of 144 respondents, 95% reported discussing private pay and 65% reported working in a clinic without a policy on this issue. There were geographic and practice-specific differences. The most common circumstance for these discussions was when a test was clinically indicated (e.g., but funding was denied) followed by when the patient initiated the conversation. The most frequently discussed tests included: multi-gene panels (73% of respondents), noninvasive prenatal testing (62%), and pre-implantation genetic diagnosis (58%). Although 65% felt it was ethical to discuss private pay, 35% indicated it was "sometimes" ethical. CONCLUSION: With the increasing availability of genomic technologies, these findings inform how we practice and demonstrate the need for policy in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".