A practical and systematic approach to organisational capacity strengthening for research in the health sector in Africa
Bibliographic record
Abstract
BACKGROUND: Despite increasing investment in health research capacity strengthening efforts in low and middle income countries, published evidence to guide the systematic design and monitoring of such interventions is very limited. Systematic processes are important to underpin capacity strengthening interventions because they provide stepwise guidance and allow for continual improvement. Our objective here was to use evidence to inform the design of a replicable but flexible process to guide health research capacity strengthening that could be customized for different contexts, and to provide a framework for planning, collecting information, making decisions, and improving performance. METHODS: We used peer-reviewed and grey literature to develop a five-step pathway for designing and evaluating health research capacity strengthening programmes, tested in a variety of contexts in Africa. The five steps are: i) defining the goal of the capacity strengthening effort, ii) describing the optimal capacity needed to achieve the goal, iii) determining the existing capacity gaps compared to the optimum, iv) devising an action plan to fill the gaps and associated indicators of change, and v) adapting the plan and indicators as the programme matures. Our paper describes three contrasting case studies of organisational research capacity strengthening to illustrate how our five-step approach works in practice. RESULTS: Our five-step pathway starts with a clear goal and objectives, making explicit the capacity required to achieve the goal. Strategies for promoting sustainability are agreed with partners and incorporated from the outset. Our pathway for designing capacity strengthening programmes focuses not only on technical, managerial, and financial processes within organisations, but also on the individuals within organisations and the wider system within which organisations are coordinated, financed, and managed. CONCLUSIONS: Our five-step approach is flexible enough to generate and utilise ongoing learning. We have tested and critiqued our approach in a variety of organisational settings in the health sector in sub-Saharan Africa, but it needs to be applied and evaluated in other sectors and continents to determine the extent of transferability.
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 | 0.475 | 0.397 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.014 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.012 | 0.040 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".