A scoping review of non-communicable disease research capacity strengthening initiatives in low and middle-income countries
Bibliographic record
Abstract
INTRODUCTION: As the epidemic of non-communicable diseases (NCDs) is rapidly developing in low and middle-income countries (LMICs), the importance of local research capacity and the role of contextually relevant research in informing policy and practice is of paramount importance. In this regard, initiatives in research capacity strengthening (RCS) are very important. The aim of this study was to review and summarize NCD research capacity strengthening strategies that have been undertaken in LMICs. METHODS: Using both systematic and other literature search, we identified and reviewed NCD-RCS initiatives that have been implemented in LMICs and reported since 2000. Information was extracted from published papers and websites related to these initiatives using a semi-structured checklist. We extracted information on program design, stakeholders involved, and countries of focus, program duration, targeted researchers, disease focus, skill/capacity areas involved and sources of funding. The extracted information was refined through further review and then underwent a textual narrative synthesis. RESULTS: We identified a number of different strategies used by research capacity strengthening programs and in the majority of initiatives, a combination of approaches was utilized. Capacity strengthening and training approaches were variously adapted locally and tailored to fit with the identified needs of the targeted researchers and health professionals. Most initiatives focused on individual level capacity and not system level capacity, although some undoubtedly benefited the research and health systems of LMICs. For most initiatives, mid-term and long-term outcomes were not evaluated. Though these initiatives might have enhanced research capacity in the immediate term, the sustainability of the results in the long-term remains unknown. CONCLUSION: Most of NCD-RCS initiatives in LMICs focused on building individual capacity and only a few focused explicitly on institutional level capacity strengthening. Though many of the initiatives appear to have had promising short-term outcomes, evidence on their long-term impact and sustainability is lacking.
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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 | 0.076 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.044 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".