Partnerships for Global Child Health
Bibliographic record
Abstract
Child mortality remains a global health challenge and has resulted in demand for expanding the global child health (GCH) workforce over the last 3 decades. Institutional partnerships are the cornerstone of sustainable education, research, clinical service, and advocacy for GCH. When successful, partnerships can become self-sustaining and support development of much-needed training programs in resource-constrained settings. Conversely, poorly conceptualized, constructed, or maintained partnerships may inadvertently contribute to the deterioration of health systems. In this comprehensive, literature-based, expert consensus review we present a definition of partnerships for GCH, review their genesis, evolution, and scope, describe participating organizations, and highlight benefits and challenges associated with GCH partnerships. Additionally, we suggest a framework for applying sound ethical and public health principles for GCH that includes 7 guiding principles and 4 core practices along with a structure for evaluating GCH partnerships. Finally, we highlight current knowledge gaps to stimulate further work in these areas. With awareness of the potential benefits and challenges of GCH partnerships, as well as shared dedication to guiding principles and core practices, GCH partnerships hold vast potential to positively impact child health.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".