Genetic Screening: A Conceptual Framework for Programmes and Policy-Making
Bibliographic record
Abstract
OBJECTIVE: Policy-makers are faced with increasing pressures from a range of different stakeholders to introduce or expand genetic screening programmes. A shared understanding is therefore needed of the many factors influencing these complex policy decisions. Our aim was to develop a theoretical framework that highlights the multiple components and influences involved in genetic screening and the policy-making process. METHODS: As part of a larger research programme, existing policy frameworks relating to genetic screening were identified through a review of the literature. Major themes were identified and synthesized into an overarching framework, which was further refined through discussions with key informants. RESULTS: The framework consists of three parts. The first part conceptualizes genetic screening as an integrated public health programme. The second part describes the policy-making process at each stage in the life cycle of the programme. The third part depicts the broader context within which policy-making occurs. CONCLUSION: This framework can support policy-makers by fostering a common understanding and facilitating dialogue with stakeholders. The framework has also been used as the conceptual foundation for the development of a more elaborate decision-guide.
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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 | 0.026 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".