Validity and usability testing of a health systems guidance appraisal tool, the AGREE-HS
Bibliographic record
Abstract
BACKGROUND: Health systems guidance (HSG) provides recommendations to address health systems challenges. No tools exist to inform HSG developers and users about the components of high quality HSG and to differentiate between HSG of varying quality. In response, we developed a tool to assist with the development, reporting and appraisal of HSG - the Appraisal of Guidelines for Research and Evaluation-Health Systems (AGREE-HS). This paper reports on the validity, usability and initial measurement properties of the AGREE-HS. METHODS: To establish face validity (Study 1), stakeholders completed a survey about the AGREE-HS and provided feedback on its content and structure. Revisions to the tool were made in response. To establish usability (Study 2), the revised tool was applied to 85 HSG documents and the appraisers provided feedback about their experiences via an online survey. An initial test of the revised tool's measurement properties, including internal consistency, inter-rater reliability and criterion validity, was conducted. Additional revisions to the tool were made in response. RESULTS: In Study 1, the AGREE-HS Overview, User Manual, quality item content and structure, and overall assessment questions were rated favourably. Participants indicated that the AGREE-HS would be useful, feasible to use, and that they would apply it in their context. In Study 2, participants indicated that the quality items were easy to understand and apply, and the User Manual, usefulness and usability of the tool were rated favourably. Study 2 participants also indicated intentions to use the AGREE-HS. CONCLUSIONS: The AGREE-HS comprises a User Manual, five quality items and two overall assessment questions. It is available at agreetrust.org.
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 | 0.218 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".