Mapping barriers and bottlenecks for personalized preventive approaches in health systems worldwide
Bibliographic record
Abstract
Abstract Over the past decades, the growing prevalence of chronic disease globally raised the public health need to plan tailored personalized preventive approaches, mainly through big data and -omic sciences. However, a conspicuous body of literature highlighted the existence of many barriers and bottlenecks hindering their actual implementation in real-word settings. From here, this scoping review of reviews aims to map all known barriers and bottlenecks to the implementation of personalized preventive approaches in European health systems and beyond. PubMed, Web of Science, Scopus and gray literature sources were consulted from 2017 to January 2023, identified barriers and bottlenecks were analyzed against the Consolidated Framework for Implementation Research (CFIR). Out of 11,602 records, 220 were deemed eligible for full-text screening, and a final sample of 34 review studies were extracted. Studies were mainly performed in USA 15 (44%) and UK 3 (9%), followed by Canada 2 (6%), India 2 (6%) and Italy 2 (6%). From our results, the lack of evidence on clinical utility, guidelines, specialized professionals, citizen trust and cultural issues are the most frequently reported barriers to the implementation of personalized preventive approaches worldwide. These in turn affect country specific policies and the applicability of such innovations across different populations, raising the risk of increasing health inequalities and discrimination concerns. Findings confirmed that most translational challenges pertain to primary and secondary prevention levels across several chronic diseases, with particular concerns for non-European ancestry individuals. Key messages • The identification of bottlenecks are informative of future precision public health interventions. • Barriers reported in most of the studies suggest the need to establish a targeted agenda.
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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 | 0.102 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".