Protocol for development of the guideline for reporting evidence based practice educational interventions and teaching (GREET) statement
Bibliographic record
Abstract
BACKGROUND: There are an increasing number of studies reporting the efficacy of educational strategies to facilitate the development of knowledge and skills underpinning evidence based practice (EBP). To date there is no standardised guideline for describing the teaching, evaluation, context or content of EBP educational strategies. The heterogeneity in the reporting of EBP educational interventions makes comparisons between studies difficult. The aim of this program of research is to develop the Guideline for Reporting EBP Educational interventions and Teaching (GREET) statement and an accompanying explanation and elaboration (E&E) paper. METHODS/DESIGN: Three stages are planned for the development process. Stage one will comprise a systematic review to identify features commonly reported in descriptions of EBP educational interventions. In stage two, corresponding authors of articles included in the systematic review and the editors of the journals in which these studies were published will be invited to participate in a Delphi process to reach consensus on items to be considered when reporting EBP educational interventions. The final stage of the project will include the development and pilot testing of the GREET statement and E&E paper. OUTCOME: The final outcome will be the creation of a Guideline for Reporting EBP Educational interventions and Teaching (GREET) statement and E&E paper. DISCUSSION: The reporting of health research including EBP educational research interventions, have been criticised for a lack of transparency and completeness. The development of the GREET statement will enable the standardised reporting of EBP educational research. This will provide a guide for researchers, reviewers and publishers for reporting EBP educational interventions.
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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 | 0.172 | 0.363 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.101 | 0.034 |
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".