Framing genetic risk: trust and credibility markers in online direct-to-consumer advertising for genetic testing
Bibliographic record
Abstract
This study looks at Internet direct-to-consumer advertising (DTCA) for genetic testing to assess the way in which genetic risk information is framed to consumers, and strategies to establish trust and credibility in this context.Keywords specific to genetic test DTC advertising were entered into popular Internet search engines, arriving at 22 companies.Representations of benefits and risks on company websites were coded and themes were developed pertaining to promotional information of genetic tests for a variety of health conditions.Two strategies were most frequently used by companies to frame risk: underlining the basis of the condition, often with genetic determinist and essentialist undertones, and stressing the commonality of the conditions.Major credibility and trust markers employed were indications of organizational professional accreditation/recognition and credentials of company executives and staff.The DTC ads examined provided limited, vague or inaccurate information about disease etiology and promoted tests for use in broader at-risk populations than is normally indicated in clinical practice.Implications of these trends for Canadian consumers and clinicians are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".