Use of Modified Delphi to Plan Knowledge Translation for Decision Makers: An Application in the Field of Advanced Practice Nursing
Bibliographic record
Abstract
Disseminating research to decision makers is difficult. Interaction between researchers and decision makers can identify key messages and processes for dissemination. To gain agreement on the key findings from a synthesis on the integration of advanced practice nurses, we used a modified Delphi process. Nursing decision makers contributed ideas via e-mail, discussed and clarified ideas face to face, and then prioritized statements. Sixteen (89%) participated and 14 (77%) completed the final phase. Priority key messages were around access to care and outcomes. The majority identified "NPs increase access to care" and "NPs and CNSs improve patient and system outcomes" as priority messaging statements. Participants agreed policy makers and the public were target audiences for messages. Consulting with policy makers provided the necessary context to develop tailored policy messages and is a helpful approach for research dissemination.
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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 | 0.222 | 0.224 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".