Consumer Health Informatics Aspects of Direct-to-Consumer Personal Genomic Testing
Bibliographic record
Abstract
This paper uses consumer health informatics as a framework to explore whether and how direct-to-consumer personal genomic testing can be regarded as a form of information which assists consumers to manage their health. It presents findings from qualitative content analysis of web sites that offer testing services, and of transcripts from focus groups conducted as part a study of the Australian public's expectations of personal genomics. Content analysis showed that service offerings have some features of consumer health information but lack consistency. Focus group participants were mostly unfamiliar with the specifics of test reports and related information services. Some of their ideas about aids to knowledge were in line with the benefits described on provider web sites, but some expectations were inflated. People were ambivalent about whether these services would address consumers' health needs, interests and contexts and whether they would support consumers' health self-management decisions and outcomes. There is scope for consumer health informatics approaches to refine the usage and the utility of direct-to-consumer personal genomic testing. Further research may focus on how uptake is affected by consumers' health literacy or by services' engagement with consumers about what they really want.
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 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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".