Australians’ views and experience of personal genomic testing: survey findings from the Genioz study
Bibliographic record
Abstract
Personal genomic tests (PGTs) for multiple purposes are marketed to ostensibly healthy people in Australia. These tests are generally marketed and purchased online commercially or can be ordered through a health professional. There has been minimal engagement with Australians about their interest in and experience with ordering a PGT. As part of a multistage, interdisciplinary project, an online survey (Stage 2 of the Genioz study) was available from May 2016 to May 2017. In total, 3253 respondents attempted the survey, with 2395 completed Australian responses from people with and without experience of having a PGT: 72% were female; 59% of the whole sample were undertaking/or had a university education; and, overall, age ranged from 18-over 80. A total of 571 respondents reported having had a genetic test, 373 of these classifiable as a PGT. A bivariate analysis suggests people who have undergone PGT in our sample were: women aged 25 and over; or in a high socioeconomic group, or have a personal or family diagnosis of a genetic condition (P ≤ 0.03). After a multivariate analysis, socioeconomic status and a genetic condition in the family were not of significance. The most common types of PGT reported were for carrier status and ancestry. Findings suggest greater awareness of, and an increasing demand for non-health related PGT in Australia. To support both consumers and health care professionals with understanding PGT results, there is a need for appropriate support and resources.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".