The Provision of Genetic Testing and Related Services in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Research in the field of genomics and genetics has evolved in recent years and so has the demand of consumers who are increasingly interested in genomic prediction of diseases and various traits. The aim of this study is to identify genetic service delivery models, policies governing the use of genomics medicine, and measures to evaluate genetic services in the province of Quebec, Canada. METHODS: questionnaire was designed and administered online in 2017 to healthcare workers with good knowledge or experience in the provision of BReast CAncer genes 1 and 2 (BRCA1/2), Lynch syndrome, familial hypercholesterolemia, inherited thrombophilia genetic tests, engaged in policy planning or evaluation of genetic services. A quali-quantitative analysis of the survey results was performed. RESULTS: Thirty professionals participated in the study. The delivery models are classified in five categories according to the leading role of healthcare professionals in patient care pathways: i) the geneticist model; ii) the primary care model; iii) the medical specialist model; iv) the population screening program model; and v) the direct-to-consumer model. Barriers to genetic services are the coverage of genetic tests by the publicly funded healthcare system, the availability of qualified personnel, and the number of genetic centers. Regulatory oversight concerning the provision of genetic services appears to be insufficient. CONCLUSIONS: Integration between genetics and the overall healthcare system in Quebec is in an early phase. Current models of genetic services require good level of genetic knowledge by all medical specialists, collaboration among different healthcare personnel, and work redistribution. The proper implementation of genomics into healthcare can be achieved through education and training, proper regulatory oversight, genomic policies, and public awareness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".