Awareness of Genetic Counseling and Perceptions of its Purpose: A Survey of the Canadian Public
Bibliographic record
Abstract
Genetic counseling can result in better outcomes when clients understand what to expect, and at least theoretically, at some point in their lifespan, anyone could be referred for or benefit from genetic counseling. Thus, in order to identify (and ultimately address) issues around awareness of genetic counseling and perceptions of its purpose, we surveyed the Canadian general population. We acquired 1,000 telephone numbers corresponding to a demographically representative sample of Canada from Survey Sampling International, and invited individuals to participate in a telephone-based survey. We administered a purpose-designed survey (in either French or English) comprising questions regarding: demographics, whether or not the individual had heard of genetic counseling, and 15 Likert scale-rated (strongly disagree-strongly agree) items about the possible purposes of genetic counseling. Responses to these 15 items were used to generate a total "knowledge score". Of the 1,000 numbers, n = 372 could not be reached, and the survey was successfully administered to n = 188 individuals (response rate 30 %). Most respondents (n = 129, 69 %) had not heard of genetic counseling, and substantial proportions thought that genetic counseling aims to prevent genetic diseases and abnormalities, help couples have children with desirable characteristics, and help people to understand their ancestry. These data could be used to inform the strategy for development of future awareness efforts, and as a baseline from which to measure their effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".