Identification of Delivery Models for the Provision of Predictive Genetic Testing in Europe: Protocol for a Multicentre Qualitative Study and a Systematic Review of the Literature
Bibliographic record
Abstract
INTRODUCTION: The appropriate application of genomic technologies in healthcare is surrounded by many concerns. In particular, there is a lack of evidence on what constitutes an optimal genetic service delivery model, which depends on the type of genetic test and healthcare context considered. The present project aims to identify, classify, and evaluate delivery models for the provision of predictive genetic testing in Europe and in selected Anglophone extra-European countries (the USA, Canada, Australia, and New Zealand). It also sets out to survey the European public health community's readiness to incorporate public health genomics into their practice. MATERIALS AND EQUIPMENT: The project consists of (i) a systematic review of published literature and selected country websites, (ii) structured interviews with health experts on the genetic service delivery models in their respective countries, and (iii) a survey of European Public Health Association (EUPHA) members' knowledge and attitudes toward genomics applications in clinical practice. The inclusion criteria for the systematic review are that articles be published in the period 2000-2015; be in English or Italian; and be from European countries or from Canada, the USA, Australia, or New Zealand. Additional policy documents will be retrieved from represented countries' government-affiliated websites. The results of the research will be disseminated through the EUPHA network, the Italian Network for Genomics in Public Health (GENISAP), and seminars and workshops. EXPECTED IMPACT OF THE STUDY ON PUBLIC HEALTH: The transfer of genomic technologies from research to clinical application is influenced not only by several factors inherent to research goals and delivery of healthcare but also by external and commercial interests that may cause the premature introduction of genetic tests in the public and private sectors. Furthermore, current genetic services are delivered without a standardized set of process and outcome measures, which makes the evaluation of healthcare services difficult. The present study will identify and classify delivery models and, subsequently, establish which are appropriate for the provision of predictive genetic testing in Europe by comparing sets of process and outcome measures. In this way, the study will provide a basis for future recommendations to decision makers involved in the financing, delivery, and consumption of genetic services.
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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 | 0.188 | 0.118 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".