Development of the AGREE II, part 1: performance, usefulness and areas for improvement
Bibliographic record
Abstract
BACKGROUND: We undertook research to improve the AGREE instrument, a tool used to evaluate guidelines. We tested a new seven-point scale, evaluated the usefulness of the original items in the instrument, investigated evidence to support shorter, tailored versions of the tool, and identified areas for improvement. METHOD: We report on one component of a larger study that used a mixed design with four factors (user type, clinical topic, guideline and condition). For the analysis reported in this article, we asked participants to read a guideline and use the AGREE items to evaluate it based on a seven-point scale, to complete three outcome measures related to adoption of the guideline, and to provide feedback on the instrument's usefulness and how to improve it. RESULTS: Guideline developers gave lower-quality ratings than did clinicians or policy-makers. Five of six domains were significant predictors of participants' outcome measures (p < 0.05). All domains and items were rated as useful by stakeholders (mean scores > 4.0) with no significant differences by user type (p > 0.05). Internal consistency ranged between 0.64 and 0.89. Inter-rater reliability was satisfactory. We received feedback on how to improve the instrument. INTERPRETATION: Quality ratings of the AGREE domains were significant predictors of outcome measures associated with guideline adoption: guideline endorsements, overall intentions to use guidelines, and overall quality of guidelines. All AGREE items were assessed as useful in determining whether a participant would use a guideline. No clusters of items were found more useful by some users than others. The measurement properties of the seven-point scale were promising. These data contributed to the refinements and release of the AGREE II.
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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 | 0.102 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".