Assessment of the impact of a clinical and health services research call in Catalonia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.072 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it