What will it take to implement health and health-related sustainable development goals?
Bibliographic record
Abstract
### Summary box In two previous publications, we have described and summarised key findings from the global systematic review and country consultations related to our assessment of the progress in implementing the health and health-related sustainable development goals (HHSDGs). Although it has been only 5 years but current evidence on the implementation of sustainable development goal (SDG) evaluations to date suggests that a vast majority of countries are off-target in relation to several outcome indicators1–3 and there are no clear strategies for integration across health and other sectors. This paper will summarise the key learnings from this exercise and propose a strategy for enhancing integration and implementation of HHSDGs in low-income and middle-income countries (LMICs). Our systematic review4 of the global evidence on the implementation of HHSDGs highlighted several important factors: 1. There are as yet no standardised metrics regarding progress and implementation globally that cover HHSDGs. The Institute of Health Metrics and Evaluation has developed and proposed a global SDG index, which has also been used to track progress5; however, this has as yet not received widespread acceptance or recognition. At …
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 | 0.031 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".