Bibliographic record
Abstract
West Africa was the focus of global attention during the Ebola virus disease outbreak, when systemic health system weaknesses compounded a serious emergency and complicated response efforts. Following the crisis, calls were made to strengthen health systems, but investments to date have fallen short of delivering the support needed to build strong health systems able to prevent and manage future outbreaks.In part, this reality serves to highlight the shortcomings of the solutions being repeatedly prioritised by external funders and experts, solutions that often fail to consider the wealth of West African evidence and actors actively working to strengthen the leadership and health systems needed to drive and sustainably improve national health outcomes. Unfortunately, this knowledge and experience are rarely heard in the global arena.This journal supplement is a contribution, although small, to changing this practice by putting the perspectives, experiences and knowledge of West Africans on the table. It presents findings from a series of research and capacity development projects in West Africa funded by the International Development Research Centre's Maternal and Child Health programme (formerly Governance for Equity in Health Systems).The evidence presented here centres around two key themes. First, the theme that context matters. The evidence shows how context can change the shape of externally imposed interventions or policies resulting in unintended outcomes. At the same time, it highlights evidence showing how innovative local actors are developing their own approaches, usually low-cost and embedded in the context, to bring about change. Second, the collection of articles discusses the critical need to overcome the existing fragmentation of expertise, knowledge and actors, and to build strong working relationships amongst all actors so they can effectively work together to identify priority issues that can realistically be addressed given the available windows of opportunity.Vibrant West African-led collaborations amongst researchers, decision-makers and civil society, which are effectively supported by national, regional and global funding, need to foster, strengthen and use locally-generated evidence to ensure that efforts to strengthen health systems and improve regional health outcomes are successful. The solutions are clearly not to be found in the 'travelling models' of standardised interventions.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".