The impact of the epidemiology of multimorbidity on health policy across settings and countries
Bibliographic record
Abstract
Abstract Issue/Problem The epidemiology of multiple health conditions (also known as multimorbidity) is increasingly being studied across many settings and countries around the world. More specifically, prevalence and predictors of multimorbidity are being explored among clinical and population-based samples from high-, middle- and low-income countries in rural and urban settings. While the prevalence of multimorbidity tends to increase among ageing populations, a growing proportion of adults under the age of 65 years are also living with multimorbidity, indicating a need for effective interventions. Description of Problem There is a need to identify consistent and distinct epidemiological patterns of multimorbidity across different settings and countries. More specifically, the prevalence and consequence of multimorbidity may have different implications in high-, middle- and low-income countries, as well as between rural and urban settings. This understanding can be informed by extracting information from relevant publications and conducting a survey of multimorbidity researchers on the impact of this research on subsequent health policy priorities and decisions in their specific setting or country. Intended Results The responses and insights provided by multimorbidity researchers from the completion of the survey (consisting of both open- and closed-ended questions) will be presented, including how the epidemiology of multimorbidity has been conducted to date and how this research may or may not be informing policy priorities and decisions in their respective countries. As well, the focus of interventions to prevent multimorbidity occurrence will be explored across settings. Lessons Overall, this presentation will aim to summarize key patterns in both epidemiology and policy responses to multimorbidity occurrence and implications across settings and countries. This work will also aim to create a more formal community of multimorbidity researchers around the world.
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 | 0.042 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".