Development of the AGREE II, part 1: performance, usefulness and areas for improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it