Enhancing review criteria for dissemination and implementation science grants
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
BACKGROUND: The existing grant review criteria do not consider unique methods and priorities of Dissemination and Implementation Science (DIS). The ImplemeNtation and Improvement Science Proposals Evaluation CriTeria (INSPECT) scoring system includes 10 criteria based on Proctor et al.'s "ten key ingredients" and was developed to support the assessment of DIS research proposals. We describe how we adapted INSPECT and used it in combination with the NIH scoring system to evaluate pilot DIS study proposals through our DIS Center. METHODS: We adapted INSPECT to broaden considerations for diverse DIS settings and concepts (e.g., explicitly including dissemination and implementation methods). Five PhD-level researchers with intermediate to advanced DIS knowledge were trained to conduct reviews of seven grant applications using both the INSPECT and NIH criteria. The INSPECT overall scores range from 0 to 30 (higher scores are better), and the NIH overall scores range from 1 to 9 (lower scores are better). Each grant was independently reviewed by two reviewers, then discussed in a group meeting to compare the experiences using both criteria to evaluate the proposal and to finalize scoring decisions. A follow-up survey was sent to grant reviewers to solicit further reflections on each scoring criterion. RESULTS: Averaged across reviewers, the INSPECT overall scores ranged from 13 to 24, while the NIH overall scores ranged from 2 to 5. Reviewer reflections highlighted the unique value and utility for each scoring criterion. The NIH criteria had a broad scientific purview and were better suited to evaluate more effectiveness-focused and pre-implementation proposals not testing implementation strategies. The INSPECT criteria were easier to rate in terms of the quality of integrating DIS considerations into the proposal and to assess the potential for generalizability, real-world feasibility, and impact. Overall, reviewers noted that INSPECT was a helpful tool to guide DIS research proposal writing. CONCLUSIONS: We confirmed complementarity in using both scoring criteria in our pilot study grant proposal review and highlighted the utility of INSPECT as a potential DIS resource for training and capacity building. Possible refinements to INSPECT include more explicit reviewer guidance on assessing pre-implementation proposals, providing reviewers with the opportunity to submit written commentary with each numerical rating, and greater clarity on rating criteria with overlapping descriptions.
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 | 0.022 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it