Examining existing strategies to prevent multimorbidity – a scoping review
Bibliographic record
Abstract
Abstract Multimorbidity has been acknowledged as the “defining challenge” for health systems around the world, but current research and policy are primarily focused on the management of multimorbidity after it occurs or the prevention of adverse events in those individuals who are already living with multimorbidity. There is still a significant need to develop and establish effective and equitable primary prevention strategies in order to avoid the occurrence of multimorbidity across populations. This scoping review aims to identify existing strategies, programs and policies that are focused on the prevention of multimorbidity across various settings and countries around the world. To identify relevant publications, two databases will be searched: PubMed and Embase. The search strategies will be adjusted for each database and will include variations of the keywords multimorbidity, prevention and strategy. This review will specifically include publications that are original research, focused on multimorbidity and published in English. However, there will be no restrictions on the location of the research or the age of the target sample or population. For the full text screening phase, more specific criteria will apply and information will be extracted from the final set of retained studies. This information will be summarized and will include key factors such as study design, study setting, type of prevention programs, scope of programs (e.g. national, regional, local, etc.) and target population (e.g. age groups, socioeconomic groups, etc.). This scoping review will describe existing prevention programs and highlight areas of gaps and opportunities in the prevention of multimorbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".