Supplementary Material for: “Should I let them know I have this?”: Multifaceted genetic discrimination and limited awareness of legal protections amongst individuals with hereditary cancer syndromes
Bibliographic record
Abstract
Introduction: Hereditary cancer syndromes (HCS), such as Hereditary Breast and Ovarian Cancer Syndrome (HBOC) and Lynch Syndrome (LS), represent approximately 10% of all cancers. Along with medical burdens associated with the genetic risk of developing cancer, many individuals face stigma and discrimination. Genetic discrimination refers to negative treatment, unfair profiling or harm based on genetic characteristics, manifesting as “felt” stigma (ostracization without discriminatory acts) or “enacted” stigma (experiencing discriminatory acts). This study aimed to describe concerns and experiences of genetic discrimination faced by individuals with HCS. Methods: Semi-structured qualitative interviews were conducted with individuals with molecularly-confirmed HCS residing in Ontario, British Columbia and Newfoundland & Labrador, Canada. Purposive sampling was applied to obtain a diverse sample across demographic characteristics. Study procedures were informed by interpretive description; data were thematically analyzed using constant comparison. Results: 73 participants were interviewed (39 HBOC, 34 LS; 51 females, 21 males, 1 gender-diverse; aged 25-80). Participants described multifaceted forms of genetic discrimination across healthcare, insurance, employment, and family/social settings. Participants valued the Genetic Non-Discrimination Act’s protective intent, but demonstrated limited knowledge of its existence and provisions. Limited knowledge, coupled with policy constraints in non-legislatable settings and third-party use of proxy genetic information, hindered participants’ ability to whistleblow or seek recourse. Conclusion: Our results illuminate a disconnect between intended protective effects of genetic non-discrimination legislation and ongoing genetic discrimination faced by individuals with hereditary conditions. To better support these individuals, this study encourages public outreach and knowledge translation efforts to increase awareness of non-discrimination legal protections.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.301 | 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".