Unlocking the Power of Healthy Longevity: Compendium of Research for the Healthy Longevity Initiative
Bibliographic record
Abstract
Noncommunicable diseases (NCDs) are among the major health and development challenges of our time. Every year, about 41 million people die due to NCDs. This makes up about 74 percent of all deaths globally, the majority of which are in low- and middle-income countries (LMICs). Countless more people live with NCDs every day. Yet, NCDs are largely treatable and preventable. The risk of developing NCDs and deaths from them can both be lowered with appropriate attention to prevention and treatment. However, weak health systems and limited access to affordable care and information, especially in LMICs, contribute to lapses in seeking and receiving appropriate and timely care. This compendium is a compilation of 18 chapters, each exploring a different but related topic in the nexus of NCDs, human capital, and productivity. It is based on a series of analytical work taken up by the World Bank to support the Healthy Longevity Initiative (HLI) - a collaborative effort between the World Bank, the University of Toronto, and key academic and development partners including the Harvard University and the University of Washington. The HLI presents one of a growing set of efforts to increase the urgency of policy response to NCDs across 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.025 |
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".