BETTER LIFE- guidelines for chronic disease preventive care for people aged 18–39 years: a literature review
Bibliographic record
Abstract
BACKGROUND: The original 'BETTER' (Building on Existing Tools To Improve Chronic Disease Prevention and Screening in Primary Care) approach consisted of a prevention-focused visit between participants aged 40-65 years and a "Prevention Practitioner" (PP), who empowered the participant to set achievable prevention and screening goals for cancers and chronic diseases. BETTER was successfully adapted for economically deprived communities (BETTER HEALTH) in Canada. Our objective was to conduct a review of guidelines in preparation for adapting the 'BETTER HEALTH' approach for younger adults aged 18-39 years living with lower income, a group known to have earlier mortality due to a higher prevalence of preventable chronic diseases than their peers with higher income. METHODS: We searched multiple electronic databases and grey literature for clinical practice guidelines on prevention/screening and included those that met the following criteria: published in English from 2008-2020 in Canada or any of the following countries (Australia, Ireland, New Zealand, Scotland, United States and England); and addressed prevention or screening. We assessed quality using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool and extracted data (publication details, recommendations, and Quality/Level of evidence as reported by authors) from sources with overall scores of 5 or higher. Final recommendations were compiled after harmonization with input from diverse stakeholders (co-investigators, PPs, and the Community Advisory Committee). RESULTS: We included a total of 85 guidelines, and developed a final list of 42 recommendations for 18-39 year-olds across 21 topics. Specific recommendations fell under the following topics: cancers, cardiovascular disease, diabetes, obesity, lifestyle (alcohol; healthy nutrition/physical activity); healthy relationships and healthy sexuality, immunization, oral health, social determinants of health, and substance use. CONCLUSION: We identified evidence-based guidelines on individual-level prevention/screening actions for adults 18-39 years old and relevant for those living with lower income which will directly inform development and implementation of the BETTER LIFE intervention.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".