Assessment of health systems guidance using the Appraisal of Guidelines for Research and Evaluation – Health Systems (AGREE-HS) instrument
Bibliographic record
Abstract
Health systems guidance (HSG) documents contain systematically developed statements or recommendations intended to address a health system challenge. The concept of HSG is fairly new and considerable effort has been undertaken to build tools to support the contextualization of recommendations. One example is the Appraisal of Guidelines for REsearch and Evaluation - Health Systems (AGREE-HS), created by international stakeholders and researchers, to assist in the development, reporting and evaluation of HSG. Here, we present the quality appraisal of 85 HSG documents published from 2012 to 2017 using the AGREE-HS. The AGREE-HS consists of five items (Topic, Participants, Methods, Recommendations, and Implementability), which are scored on a 7-point response scale (1=lowest quality; 7=highest quality). Overall, AGREE-HS item scores were highest for the 'Topic' and 'Recommendations' items (means above the mid-point of 4), while the 'Participants', 'Methods', and 'Implementability' items received lower scores. Documents without a specific health focus and those authored by the National Institute for Health and Care Excellence group, achieved higher AGREE-HS overall scores than their comparators. No statistically significant changes in overall scores were observed over time. This is the first time that the AGREE-HS has been applied, providing a current quality status report of HSG and identifying where improvements in HSG development and reporting can be made.
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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 | 0.538 | 0.638 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".