Concocting that Magic Elixir: Successful Grant Application Writing in Dissemination and Implementation Research
Bibliographic record
Abstract
BACKGROUND: This paper reports core competencies for dissemination and implementation (D&I) grant application writing and provides tips for writing a successful proposal. METHODS: Two related phases were used to collect the data: a card sorting process among D&I researchers and an expert review among a smaller set of researchers. Card sorting was completed by 123 respondents. In the second phase, a series of grant application writing tips were developed based on the combined 170 years of grant review experience of the writing team. RESULTS: The card sorting resulted in 12 core competencies for D&I grant application writing that covered the main sections in a grant application to the US National Institutes of Health: (a) specific aims that provide clear rationale, objectives, and an overview of the research plan; (b) significance that frames and justifies the importance of a D&I question; (c) innovation that articulates novel products and new knowledge; and (d) approach that uses a relevant D&I model, addresses measurement and the D&I context, and includes an analysis plan well-tied to the aims and measures. CONCLUSIONS: Writing a successful D&I grant application is a skill that can be learned with experience and attention to the core competencies articulated in this paper.
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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 | 0.274 | 0.395 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".