"Harnessing genomics to improve health in India" – an executive course to support genomics policy
Bibliographic record
Abstract
BACKGROUND: The benefits of scientific medicine have eluded millions in developing countries and the genomics revolution threatens to increase health inequities between North and South. India, as a developing yet also industrialized country, is uniquely positioned to pioneer science policy innovations to narrow the genomics divide. Recognizing this, the Indian Council of Medical Research and the University of Toronto Joint Centre for Bioethics conducted a Genomics Policy Executive Course in January 2003 in Kerala, India. The course provided a forum for stakeholders to discuss the relevance of genomics for health in India. This article presents the course findings and recommendations formulated by the participants for genomics policy in India. METHODS: The course goals were to familiarize participants with the implications of genomics for health in India; analyze and debate policy and ethical issues; and develop a multi-sectoral opinion leaders' network to share perspectives. To achieve these goals, the course brought together representatives of academic research centres, biotechnology companies, regulatory bodies, media, voluntary, and legal organizations to engage in discussion. Topics included scientific advances in genomics, followed by innovations in business models, public sector perspectives, ethics, legal issues and national innovation systems. RESULTS: Seven main recommendations emerged: increase funding for healthcare research with appropriate emphasis on genomics; leverage India's assets such as traditional knowledge and genomic diversity in consultation with knowledge-holders; prioritize strategic entry points for India; improve industry-academic interface with appropriate incentives to improve public health and the nation's wealth; develop independent, accountable, transparent regulatory systems to ensure that ethical, legal and social issues are addressed for a single entry, smart and effective system; engage the public and ensure broad-based input into policy setting; ensure equitable access of poor to genomics products and services; deliver knowledge, products and services for public health. A key outcome of the course was the internet-based opinion leaders' network - the Indian Genome Policy Forum - a multi-stakeholder forum to foster further discussion on policy. CONCLUSION: We expect that the process that has led to this network will serve as a model to establish similar Science and Technology policy networks on regional levels and eventually on a global level.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| 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".