The current stage of Italy in the implementation of genomics into the National Healthcare System: an application of the B1MG maturity level model
Bibliographic record
Abstract
Introduction: Genomics holds significant promise for prevention and clinical care yet integrating it into the national healthcare system (NHS) requires considerable system-wide changes. This study assessed the current stage of Italy in the use of genomics, to map critical areas for improvement and contribute to a strategic plan. Methods: A total of 18 experts rated individually the level of maturity of the Italian NHS on a scale from 1 (lowest) to 5 (highest) using the B1MG Maturity Level Model tool. This instrument is an European matrix of 49 indicators grouped into eight domains: governance, economic aspects, ethics and legislation, public awareness, workforce skills, clinical organization, clinical guidelines, and data infrastructure. Consensus procedures were performed within each domain to finally agree on one maturity level per indicator. Results: Despite a few national initiatives, Italy shows a local level of implementation in most indicators. Genomic medicine is considered a priority, but still lacks an updated strategy and investment plans. A higher maturity is reached for ethical and legal aspects, but there is a strong need to invest in workforce training, citizen engagement and literacy, and large-scale adoption of tools and novel technologies. Infrastructures and guidelines to improve data storage, management, analysis, interpretation, and sharing are not yet widespread available. Discussion: Italy is at the beginning of its journey towards a sustainable implementation of genomics. An updated national strategy with coordinated actions and investment plans is needed to make progress in key areas, including personnel education, public engagement, technical infrastructure, and clinical organization.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".