How to improve research capacity strengthening efforts: learning from the monitoring and evaluation of four research consortia in Africa
Bibliographic record
Abstract
Recent efforts to shift the control and leadership of health research on African issues to Africa have led to increased investments for scientific research capacity strengthening (RCS) on the continent and a greater demand for accountability, value for money and demonstration of return on investment. There is limited literature on monitoring and evaluation (M&E) of RCS systems and there is a clear need to further explore whether the M&E frameworks and approaches that are currently used are fit for purpose. The M&E approaches taken by four African RCS consortia funded under the Developing Excellence in Leadership, Training and Science in Africa (DELTAS) I initiative were assessed using several methods, including a framework comparison of the M&E approaches, semi-structured interviews and facilitated discussion sessions. The findings revealed a wide range in the number of indicators used in the M&E plans of individual consortium, which were uniformly quantitative and at the output and outcome levels. Consortia revealed that additional information could have been captured to better evaluate the success of activities and measure the ripple effects of their efforts. While it is beneficial for RCS consortia to develop and implement their own M&E plans, this could be strengthened by routine engagement with funders/programme managers to further align efforts. It is also important for M&E plans to consider qualitative data capture for assessment of RCS efforts. Efforts could be further enhanced by supporting platforms for cross-consortia sharing, particularly when trying to assess more complex effects. Consortia should make sure that processes for developmental evaluation, and capturing and using the associated learning, are in place. Sharing the learning associated with M&E of RCS efforts is vital to improve future efforts. Investing and improving this aspect of RCS will help ensure tracking of progress and impact of future efforts, and ensure accountability and the return on investment. The findings are also likely applicable well beyond health research.
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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 | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".