Welfare Genome Project: A Participatory Korean Personal Genome Project With Free Health Check-Up and Genetic Report Followed by Counseling
Bibliographic record
Abstract
The Welfare Genome Project (WGP) provided 1,000 healthy Korean volunteers with detailed genetic and health reports to test the social perception of integrating personal genetic and healthcare data at a large-scale. WGP was launched in 2016 in the Ulsan Metropolitan City as the first large-scale genome project with public participation in Korea. The project produced a set of genetic materials, genotype information, clinical data, and lifestyle survey answers from participants aged 20-96. As compensation, the participants received a free general health check-up on 110 clinical traits, accompanied by a genetic report of their genotypes followed by genetic counseling. In a follow-up survey, 91.0% of the participants indicated that their genetic reports motivated them to improve their health. Overall, WGP expanded not only the general awareness of genomics, DNA sequencing technologies, bioinformatics, and bioethics regulations among all the parties involved, but also the general public's understanding of how genome projects can indirectly benefit their health and lifestyle management. WGP established a data construction framework for not only scientific research but also the welfare of participants. In the future, the WGP framework can help lay the groundwork for a new personalized healthcare system that is seamlessly integrated with existing public medical infrastructure.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".