Research capacity strengthening: donor approaches to improving and assessing its impact in low‐ and middle‐income countries
Bibliographic record
Abstract
Increasing attention, and a concomitant increase in funds, is being devoted to the strengthening of research capacity for health within low- and middle-income countries. Yet approaches to research capacity strengthening (RCS) are still new, and there is much debate about how to strengthen something that is so difficult to define, let alone measure. This paper aims to inform our understanding of how research capacity is being strengthened, and how we might consider the effectiveness of these initiatives. It does this by examining (a) understandings of and approaches to RCS, and (b) different ways in which RCS is monitored and evaluated. The study included a literature review, internet search, and analysis of the web pages and available documents for six donor organizations key to health RCS. E-mail and telephone discussions were conducted with experts in the area of health RCS, as well as semi-structured telephone interviews with representatives from the six identified organizations. The study found that understandings of and approaches to RCS are wide ranging. We are at the early stages of knowing how best to identify, target and affect the many factors that are important for stronger research capacity. Furthermore, as RCS initiatives become more wide-ranging and complex, they become more difficult to monitor and evaluate. Donors are struggling with many challenges associated with tracking RCS initiatives. There is no consensus on the best methods or tools to use. There is a clear need for improved strategies and the development of a tried and tested framework for RCS tracking.
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 | 0.385 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.037 | 0.040 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.007 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".