P559: A novel alternate service delivery model for genetic counseling in a rural population: The New Brunswick experience
Bibliographic record
Abstract
Patients accessing genetics services in traditional in-person clinic settings have historically faced multiple barriers, including geographical, socioeconomic, and health-related challenges. Canada’s vast geography, coupled with 1 in 5 Canadians residing in rural areas, exacerbates these difficulties, as most genetics clinics are concentrated in urban regions. The COVID-19 pandemic accelerated the adoption of alternate service delivery models (SDMs) for genetic counseling, such as telephone and videoconferencing, which were found to provide care of equal quality compared to in-person counseling. We present an innovative alternate service delivery model, the first of its kind in Canada, implemented in New Brunswick, a predominantly rural province. Established in 2018, this clinic utilizes public-private partnerships to offer remote genetic counseling to residents throughout the province, enabling patients to access care including genetic testing, directly from their homes. The clinic serves nearly 600 patients annually, significantly improving the accessibility of genetics services. This approach ensures rapid access for urgent oncology patients and offers relatively short wait times for non-urgent cases. The success of this model suggests its potential application in addressing challenges related to hard-to-recruit positions, mainstreaming, and in regions lacking in-house genetics clinics. This model holds promise for improving access to genetics services for underserved populations, particularly those in rural areas across Canada.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".