Indicators for tracking programmes to strengthen health research capacity in lower- and middle-income countries: a qualitative synthesis
Bibliographic record
Abstract
BACKGROUND: The monitoring and evaluation of health research capacity strengthening (health RCS) commonly involves documenting activities and outputs using indicators or metrics. We sought to catalogue the types of indicators being used to evaluate health RCS and to assess potential gaps in quality and coverage. METHODS: We purposively selected twelve evaluations to maximize diversity in health RCS, funders, countries, and approaches to evaluation. We explored the quality of the indicators and extracted them into a matrix across individual, institutional, and national/regional/network levels, based on a matrix in the ESSENCE Planning, Monitoring and Evaluation framework. We synthesized across potential impact pathways (activities to outputs to outcomes) and iteratively checked our findings with key health RCS evaluation stakeholders. RESULTS: Evaluations varied remarkably in the strengths of their evaluation designs. The validity of indicators and potential biases were documented in a minority of reports. Indicators were primarily of activities, outputs, or outcomes, with little on their inter-relationships. Individual level indicators tended to be more quantitative, comparable, and attentive to equity considerations. Institutional and national-international level indicators were extremely diverse. Although linkage of activities through outputs to outcomes within evaluations was limited, across the evaluations we were able to construct potential pathways of change and assemble corresponding indicators. CONCLUSIONS: Opportunities for improving health RCS evaluations include work on indicator measurement properties and development of indicators which better encompass relationships with knowledge users. Greater attention to evaluation design, prospective indicator measurement, and systematic linkage of indicators in keeping with theories of change could provide more robust evidence on outcomes of health RCS.
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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: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.069 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".