Current Genetic Service Delivery Models for the Provision of Genetic Testing in Europe: A Systematic Review of the Literature
Bibliographic record
Abstract
Background: The provision of genetic services, along with research in the fields of genomics and genetics, has evolved in recent years to meet the increasing demand of consumers interested in prediction of genetic diseases and various inherited traits. The aim of this study is to evaluate genetic services in order to identify and classify delivery models for the provision of genetic testing in European and in extra-European countries. Methods: A systematic review of the literature was conducted using five electronic resources. Inclusion criteria were that studies be published in English or Italian during the period 2000-2015 and carried out in European or extra-European countries (Canada, USA, Australia, or New Zealand). Results: 148 genetic programs were identified in 117 articles and were delivered mostly in the UK (59, 40%), USA (35, 24%) or Australia (16, 11%). The programs were available nationally (66;45%), regionally (49; 33%) or in urban areas (21, 14%). Ninety-six (64%) of the programs were integrated into healthcare systems, 48 (32.21%) were pilot programs and five (3%) were direct-to-consumer genetic services. The genetic tests offered were mainly for BRCA1/2 (59, 40%), Lynch syndrome (23, 16%), and newborn screening (18, 12%). Healthcare professionals with different backgrounds are increasingly engaged in the provision of genetic services. Based on which healthcare professionals have prominent roles in the respective patient care pathways, genetic programs were classified into five models: i) the geneticists model; ii) the primary care model; iii) the medical specialists model; iv) the population screening programs model; and v) the direct-to-consumer model. Conclusions: New models of genetic service delivery are currently under development worldwide to address the increasing demand for accessible and affordable services. These models require the integration of genetics into all medical specialties, collaboration among different healthcare professionals, and the redistribution of professional roles. An appropriate model for genetic service provision in a specific setting should ideally be defined according to the type of healthcare system, the genetic test provided within a genetic program, and the cost-effectiveness of the intervention. Only applications with proven efficacy and cost-effectiveness should be implemented in healthcare systems and made available to all citizens.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".