Energize Staff to Create a Research Agenda
Bibliographic record
Abstract
There is a need to explore and align priorities for building the foundation for nursing research in any institution. The Delphi technique was chosen as means of setting priorities for nursing research. This method enlisted feedback from all levels of nurses from various practice areas and various nursing roles. A series of 3 rounds of surveys provided feedback. Mixed methods were utilized to reach consensus on the various themes/topics that emerged in each round. In the final survey round, nurses ranked the final themes as to the level of importance, which resulted in identification of the key top 5 priorities. The priorities are the foundation for a research agenda for the coming years. The nursing research council, led by the clinical nurse specialist chairperson, reviewed the results and generated a comprehensive review of literature for current evidence on each priority topic. Evidence was critiqued, rated, and resulted in research questions that formulated the research agenda. The priorities were integrated within the pillars of excellence that are the foundation of the institutions' strategic goals. Using this technique provides a beneficial and structured way to gain input from those who need to own the work of building nursing research within an institution. Positive factors, such as beginning with input from all levels, the ease of providing feedback, and using a Delphi method study to bring alignment within an organization, serve to strengthen nursing research into a much broader scope and focus.
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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 | 0.136 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".