Developing an Interview Guide to Evaluate Practice-based Evidence in Nutrition: Use of the Delphi Technique
Bibliographic record
Abstract
An interview guide was created for qualitative evaluation of the impact of Practice-based Evidence in Nutrition (PEN) on dietetic practice, and its success as a medium for knowledge translation and transfer (KTT). The Delphi technique was used to bring together a diverse group of experts (n=7) with extensive knowledge in KTT and evidence-based practice (EBP); these experts developed the interview guide content. The technique is an effective means of gathering expert input to inform evaluation tool development, particularly in the absence of accepted evaluation guidelines or pre-existing evaluation tools. Although challenges exist with the Delphi technique, it is an adaptable method that can be modified to meet a variety of needs. During this project, the technique was modified to meet specific needs, including participants' partial anonymity and starting material to reduce the number of required rounds. The resulting interview guide contained open-ended questions focused on respondents' understanding of EBP and PEN, use of PEN by dietitians in other disciplines, perceptions of the quality/usefulness of PEN, and barriers to and facilitators of PEN use.
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.
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 | 0.070 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".