A Delphi survey to determine how educational interventions for evidence-based practice should be reported: Stage 2 of the development of a reporting guideline
Bibliographic record
Abstract
BACKGROUND: Undertaking a Delphi exercise is recommended during the second stage in the development process for a reporting guideline. To continue the development for the Guideline for Reporting Evidence-based practice Educational interventions and Teaching (GREET) a Delphi survey was undertaken to determine the consensus opinion of researchers, journal editors and educators in evidence-based practice (EBP) regarding the information items that should be reported when describing an educational intervention for EBP. METHODS: A four round online Delphi survey was conducted from October 2012 to March 2013. The Delphi panel comprised international researchers, educators and journal editors in EBP. Commencing with an open-ended question, participants were invited to volunteer information considered important when reporting educational interventions for EBP. Over three subsequent rounds participants were invited to rate the importance of each of the Delphi items using an 11 point Likert rating scale (low 0 to 4, moderate 5 to 6, high 7 to 8 and very high >8). Consensus agreement was set a priori as at least 80 per cent participant agreement. Consensus agreement was initially calculated within the four categories of importance (low to very high), prior to these four categories being merged into two (<7 and ≥7). Descriptive statistics for each item were computed including the mean Likert scores, standard deviation (SD), range and median participant scores. Mean absolute deviation from the median (MAD-M) was also calculated as a measure of participant disagreement. RESULTS: Thirty-six experts agreed to participate and 27 (79%) participants completed all four rounds. A total of 76 information items were generated across the four survey rounds. Thirty-nine items (51%) were specific to describing the intervention (as opposed to other elements of study design) and consensus agreement was achieved for two of these items (5%). When the four rating categories were merged into two (<7 and ≥7), 18 intervention items achieved consensus agreement. CONCLUSION: This Delphi survey has identified 39 items for describing an educational intervention for EBP. These Delphi intervention items will provide the groundwork for the subsequent consensus discussion to determine the final inclusion of items in the GREET, the first reporting guideline for educational interventions in EBP.
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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 | 0.159 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".