Capacity for conducting systematic reviews in low- and middle-income countries: a rapid appraisal
Bibliographic record
Abstract
BACKGROUND: Systematic reviews of research are increasingly recognised as important for informing decisions across policy sectors and for setting priorities for research. Although reviews draw on international research, the host institutions and countries can focus attention on their own priorities. The uneven capacity for conducting research around the world raises questions about the capacity for conducting systematic reviews. METHODS: A rapid appraisal was conducted of current capacity and capacity strengthening activities for conducting systematic reviews in low- and middle-income countries (LMICs). A systems approach to analysis considered the capacity of individuals nested within the larger units of research teams, institutions that fund, support, and/or conduct systematic reviews, and systems that support systematic reviewing internationally. RESULTS: International systematic review networks, and their support organisations, are dominated by members from high-income countries. The largest network comprising a skilled workforce and established centres is the Cochrane Collaboration. Other networks, although smaller, provide support for systematic reviews addressing questions beyond effective clinical practice which require a broader range of methods. Capacity constraints were apparent at the levels of individuals, review teams, organisations, and system wide. Constraints at each level limited the capacity at levels nested within them. Skills training for individuals had limited utility if not allied to opportunities for review teams to practice the skills. Skills development was further constrained by language barriers, lack of support from academic organisations, and the limitations of wider systems for communication and knowledge management. All networks hosted some activities for strengthening the capacities of individuals and teams, although these were usually independent of core academic programmes and traditional career progression. Even rarer were efforts to increase demand for systematic reviews and to strengthen links between producers and potential users of systematic reviews. CONCLUSIONS: Limited capacity for conducting systematic reviews within LMICs presents a major technical and social challenge to advancing their health systems. Effective capacity in LMICs can be spread through investing effort at multiple levels simultaneously, supported by countries (predominantly high-income countries) with established skills and experience.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.781 | 0.894 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.056 | 0.049 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.032 | 0.033 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".