Bibliographic record
Abstract
Genetic knowledge holds great promise in terms of health benefits, yet also raises challenges regarding the delivery of beneficial testing and services. Addressing this challenge is especially important in rural areas where lack of access to clinical genetics is pervasive, resulting in considerable inequities in service availability. The purpose of this research study is to explore the hypothesis that a novel telehealth strategy for delivering predictive testing (PT) for Huntington disease (HD) can address the potential inequity that exists in access to PT in rural communities in British Columbia. To address the hypothesis, the project employed a three part, highly structured, mixed method sequential exploratory approach. The first part of the research involved: a) a mapping study; b) a qualitative interview study of 33 at-risk individuals; c) a survey of 102 individuals at-risk for HD; and d) an 11-person expert workshop. The second part of the research built on results from the prior work and was to develop a telehealth PT protocol and a HD PT dedicated website for individuals considering testing. The final component of the research involved a pilot project that compared the novel telehealth protocol with the standard, Vancouver-based PT protocol. Evaluation of the pilot project was conducted by quantitative survey with 28 participants and was subject to statistical analysis. Results revealed that PT rates are lower in rural areas and that access is a significant issue due to distance related factors and the inflexible nature of the PT process. The pilot project demonstrated that providing PT via telehealth is not only possible, but is also warranted. There were no significant differences in terms of quality of care, information, counseling and support during the PT process between the Vancouver-tested and telehealth-tested groups. Overall, the pilot study reveals that providing PT via telehealth can improve access to PT while maintaining high quality of care and support. The work adds to a growing body of literature on the utility of telehealth services in genomic medicine in an age of increased technological innovation and comfort with such communication mechanisms.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".