Climate for Evidence-Informed Health Systems: A Profile of Systematic Review Production in 41 Low- and Middle-Income Countries, 1996-2008
Bibliographic record
Abstract
OBJECTIVE: To describe systematic review production in 41 countries in Africa, the Americas, Asia and the eastern Mediterranean to understand one dimension of the climate for evidence-informed health systems and to provide a baseline for an evaluation of knowledge translation initiatives. METHODS: Our focus was systematic reviews published between 1996 and 2008 that had a corresponding author based in, or that appeared to target, one of the countries in these regions. We searched both Medline and Embase using validated search strategies, identified citations with a country name in the corresponding author's institutional affiliation or as a textword (i.e., an explicit mention in the title or abstract) or keyword, and coded articles describing a systematic review. We followed the same citation identification procedure for Health Systems Evidence, a database containing systematic reviews about health systems. RESULTS: Systematic review production increased between three-fold (for Africa in Medline) and 110-fold (for Asia in Embase) between the first period (1996-2002) and second period (2003-2008). In the second period, China was more often the home of corresponding authors and the target of reviews than any other country. No systematic reviews were produced by a corresponding author based in nine countries, or appeared to target five countries. Only 48 reviews identified through Medline and Embase addressed health systems, and 35 health systems reviews identified through Health Systems Evidence addressed these countries. CONCLUSION: In many countries, those seeking to support evidence-informed health systems cannot turn to experienced local systematic reviewers to help them to find and use systematic reviews or to conduct reviews on high priority topics when none exists. These findings suggest the need for local capacity-building initiatives.
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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 | 0.126 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.074 | 0.117 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".