Assessing the Impact of Health Research in Saudi Arabia: A Cross-Sectional Survey of Researchers’ Perceptions of Importance and Implementation Barriers
Bibliographic record
Abstract
Introduction: Engaging researchers early in the process of research assessment increases transparency and builds a sense of ownership, which ensures successful implementation and sustainability and fosters a healthier research culture. In anticipation of the potential implementation of research impact assessment in Saudi Arabia, the aim of this study was to explore the views of healthcare researchers regarding (i) the perceived importance of different categories and indicators used to track health research impacts, (ii) the purposes of research impact assessment, and (iii) the barriers associated with implementing research impact assessment. Methods: A Web-based cross-sectional survey was conducted between June 8 and September 20, 2023. Academic and nonacademic researchers (n = 110) working in Saudi Arabia completed the questionnaire. The categories and indicators used to capture the diverse ways in which research impacts arise were drawn from the Payback Framework and the Canadian Academy of Health Sciences framework. We used descriptive statistics to report demographic characteristics and perceptions and t tests or ANOVA to examine the impact of respondent characteristics on their ratings of the importance of impact indicators. The data were analyzed using IBM SPSS 30. Results: Among the 110 respondents, 91 (82.7%) supported the move toward research impact assessment. Among the indicators used to track research impact, publications in peer-reviewed journals and contributions to better health outcomes were assigned the highest level of importance. The most commonly selected purpose for impact assessment was to allocate funds to projects that are more likely to benefit society and the economy (n = 85, 77%). The most commonly reported barrier to assessment implementation was the extensive resources needed to collect data (68%), followed by researchers’ perception that existing research is not driven by the health needs of the Saudi community (66.4%). Conclusions: Our findings suggest that, in general, Saudi researchers support a move toward impact assessment, but they have clear preferences for the indicators measured to capture impact and for the purpose of impact assessment. Strategies to counter the expected barriers to impact assessment, such as those suggested in our paper, need to be explored and addressed by decision-makers planning for impact assessment in the Saudi context.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".