Health system guidance appraisal—concept evaluation and usability testing
Bibliographic record
Abstract
BACKGROUND: Health system guidance (HSG) provides recommendations aimed to address health system challenges. However, there is a paucity of methods to direct, appraise, and report HSG. Earlier research identified 30 candidate criteria (concepts) that can be used to evaluate the quality of HSG and guide development and reporting requirements. The objective of this paper was to describe two studies aimed at evaluating the importance of these 30 criteria, design a draft HSG appraisal tool, and test its usability. METHODS: This study involved a two-step survey process. In step 1, respondents rated the 30 concepts for appropriateness to, relevance to, and priority for health system decisions and HSG. This led to a draft tool. In step 2, respondents reviewed HSG documents, appraised them using the tool, and answered a series of questions. Descriptive analyses were computed. RESULTS: Fifty participants were invited in step 1, and we had a response rate of 82 %. The mean response rates for each concept within each survey question were universally favorable. There was also an overall agreement about the need for a high-quality tool to systematically direct the development, appraisal, and reporting of HSG. Qualitative feedback and a consensus process by the team led to refinements to some of the concepts and the creation of a beta (draft) version of the HSG tool. In step 2, 35 participants were invited and we had a response rate of 74 %. Exploratory analyses showed that the quality of the HSGs reviewed varied as a function of the HSG item and the specific document assessed. A favorable consensus was reached with participants agreeing that the HSG items were easy to understand and easy to apply. Moreover, the overall agreement was high for the usability of the tool to systematically direct the development (85 %), appraisal (92 %), and reporting (81 %) of HSG. From this process, version 1.0 of the HSG appraisal tool was generated complete with 32 items (and their descriptions) and 4 domains. CONCLUSIONS: The final tool, named the Appraisal of Guidelines for Research and Evaluation for Health Systems (AGREE-HS) (version 1), defines expectations of HSG and facilitates informed decisions among policymakers on health system delivery, financial, and governance arrangements.
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 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 | 0.039 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".