Frameworks for evaluating health research capacity strengthening: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Health research capacity strengthening (RCS) projects are often complex and hard to evaluate. In order to inform health RCS evaluation efforts, we aimed to describe and compare key characteristics of existing health RCS evaluation frameworks: their process of development, purpose, target users, structure, content and coverage of important evaluation issues. A secondary objective was to explore what use had been made of the ESSENCE framework, which attempts to address one such issue: harmonising the evaluation requirements of different funders. METHODS: We identified and analysed health RCS evaluation frameworks published by seven funding agencies between 2004 and 2012, using a mixed methods approach involving structured qualitative analyses of documents, a stakeholder survey and consultations with key contacts in health RCS funding agencies. RESULTS: The frameworks were intended for use predominantly by the organisations themselves, and most were oriented primarily towards funders' internal organisational performance requirements. The frameworks made limited reference to theories that specifically concern RCS. Generic devices, such as logical frameworks, were typically used to document activities, outputs and outcomes, but with little emphasis on exploring underlying assumptions or contextual constraints. Usage of the ESSENCE framework appeared limited. CONCLUSIONS: We believe that there is scope for improving frameworks through the incorporation of more accessible information about how to do evaluation in practice; greater involvement of stakeholders, following evaluation capacity building principles; greater emphasis on explaining underlying rationales of frameworks; and structuring frameworks so that they separate generic and project-specific aspects of health RCS evaluation. The third and fourth of these improvements might assist harmonisation.
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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 | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.195 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
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".