Assessment of the impact of a clinical and health services research call in Catalonia
Bibliographic record
Abstract
This article presents the ex-post assessment of a program of clinical and health services research and the evaluation of the social impact. The Catalan Agency for Health Information, Assessment, and Quality (CAHIAQ) promotes a biannual open, public, competitive extramural research call to conduct non-commercial clinical and health services research. Its aim is to address local needs of research (knowledge gaps) and to assess the implementation of innovation. Approximately 5.8 million Euros have been allocated to the call. To meet the Agency’s mission, a periodical ‘call for expressions of interest’ and topic prioritization is organized prior to the research call. The awarded projects are submitted to an ex-ante, ongoing, and ex-post assessment. Impact assessment of the research call on advancing knowledge and healthcare decision making is based on the Canadian Academy of Health Sciences framework (Panel on Return on Investment in Health Research, 2009). The methods used include bibliometric analysis, surveys to researchers and decision-makers, and a more in-depth case study of translation pathways. This includes a crossover of cases from 1996 to 2004. Some results are compared against other international health services research calls. The conclusion is that local agencies can significantly contribute to fill knowledge gaps in a specific context. Assessment of the complete research cycle provides opportunities for improving the entire research process (identification of knowledge needs, call for proposals, funding allocation, research completion, subsequent impact). Specifically, assessment of the different types of impact of research development on knowledge generation and decision making closes the evaluation cycle fulfilling the Agency's mission.
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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 | 0.070 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".