Designing an Impact-Oriented Model of Research and Technology Evaluation: An Experience of I.R.Iran.
Bibliographic record
Abstract
Background: Research impact assessment is already being institutionalized in health research and innovation systems. In developing countries, there are many different research assessment models which have focused more on research output in academic levels and less on impact. Objective: The aim of this study is designing an Iranian impact-oriented model of research and technology evaluation. Method: This is a mixed study. In the quantitative part, by reviewing the literature, a list of research impact indicators that existed were gathered, reviewed, and scored by participants on importance, relevance, and measurability via a 5-point Likert scale. All indicators with a mean score equal to or greater than 3.5 entered the qualitative part, which were discussed in depth by engaging key stakeholders regarding their validity and feasibility through focus groups, interviews, and expert panels. Results: The Iranian research impact evaluation model was developed with four main pillars (including input and process, output, outcome, and impact), four areas (stewardship, advancing knowledge and translation, technology, and impact), and 30 indicators through key stakeholders participation in the Iranian health research system. Conclusions: This model has been introduced as the first model designed to evaluate the impact of health research and can be one of the most important tools for allocating limited funding resources while maximizing the desired impact of research in the community.
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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 | 0.066 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".