Evaluating global health initiatives to improve health equity
Bibliographic record
Abstract
Global health initiatives are multistakeholder partnerships that mobilize and disburse resources to address global health challenges, often by supporting implementation of health programmes in low-and middle-income countries.1 These initiatives have made enormous contributions to saving lives and improving health globally, and are vital to the realization of sustainable development goal (SDG) 3 to ensure healthy lives and promote well-being for all at all ages.1,2 However, some members of the global health community have criticized the ways these initiatives work, notably in relation to power imbalances between donor and implementing partners in priority-setting and decision-making. 1 These imbalances can translate into questions of whose knowledge, vision and voice drive organizational direction.We are writing as representatives of the evaluation units and evaluation advisory bodies of three prominent global health initiatives, to reflect on challenges and solutions to strengthening health equity via improved evaluation.As part of its mission to save lives and protect people's health by increasing equitable and sustainable use of vaccines, Gavi, the Vaccine Alliance, helps vaccinate almost half the world's children against deadly and debilitating infectious diseases.3 To ensure that all women, children and adolescents can survive and thrive, the Global Financing Facility for Women, Children and Adolescents, a multistakeholder global partnership housed at the World Bank, supports 36 low-and lower-middle-income countries with financing and technical assistance to develop and implement prioritized national health plans to scale up access to affordable, quality care.4 The Global Fund, a worldwide partnership
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 | 0.174 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".