A step forward, but still inadequate: Australian health professionals’ views on the genetics and life insurance moratorium
Bibliographic record
Abstract
BACKGROUND: In 2019, the Australian life insurance industry introduced a partial moratorium (ban) limiting the use of genetic test results in life insurance underwriting. The moratorium is industry self-regulated and applies only to policies below certain financial limits (eg, $500 000 of death cover). METHODS: We surveyed Australian health professionals (HPs) who discuss genetic testing with patients, to assess knowledge of the moratorium; reported patient experiences since its commencement; and HP views regarding regulation of genetic discrimination (GD) in Australia. RESULTS: Between April and June 2020, 166 eligible HPs responded to the online survey. Of these, 86% were aware of the moratorium, but <50% had attended related training/information sessions. Only 16% answered all knowledge questions correctly, yet 69% believed they had sufficient knowledge to advise patients. Genetics HPs' awareness and knowledge were better than non-genetics HPs' (p<0.05). There was some reported decrease in patients delaying/declining testing after the moratorium's introduction, however, 42% of HPs disagreed that patients were more willing to have testing post-moratorium. Although many (76%) felt the moratorium resolved some GD concerns, most (88%) still have concerns, primarily around self-regulation, financial limits and the moratorium's temporary nature. Almost half (49%) of HPs reported being dissatisfied with the moratorium as a solution to GD. The majority (95%) felt government oversight is required, and 93% felt specific Australian legislation regarding GD is required. CONCLUSION: While the current Australian moratorium is considered a step forward, most HPs believe it falls short of an adequate long-term regulatory solution to GD in life insurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".