Concocting that Magic Elixir: Successful Grant Application Writing in Dissemination and Implementation Research
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: 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.
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 | 0.027 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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