Workforce Implications of Increased Referrals to Hereditary Cancer Services in Canada: A Scenario-Based Analysis
Bibliographic record
Abstract
Over the last decade, utilization of clinical genetics services has grown rapidly, putting increasing pressure on the workforce available to deliver genetic healthcare. To highlight the policy challenges facing Canadian health systems, a needs-based workforce requirements model was developed to determine the number of Canadian patients in 2030 for whom an assessment of hereditary cancer risk would be indicated according to current standards and the numbers of genetic counsellors, clinical geneticists and other physicians with expertise in genetics needed to provide care under a diverse set of scenarios. Our model projects that by 2030, a total of 90 specialist physicians and 326 genetic counsellors (1.7-fold and 1.6-fold increases from 2020, respectively) will be required to provide Canadians with indicated hereditary cancer services if current growth trends and care models remain unchanged. However, if the expansion in eligibility for hereditary cancer assessment accelerates, the need for healthcare providers with expertise in genetics would increase dramatically unless alternative care models are widely adopted. Increasing capacity through service delivery innovation, as well as mainstreaming of cancer genetics care, will be critical to Canadian health systems' ability to meet this challenge.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".