The perceptions and experience of developing patient (version of) guidelines: a descriptive qualitative study with Chinese guideline developers
Bibliographic record
Abstract
OBJECTIVE: To understand developers' perception of patient (versions of) guidelines (PVGs), and identify challenges during the PVG development, with the aim to inform methodological guidance for future PVG development. METHODS: We used a descriptive qualitative design. Semi-structured interviews were conducted virtually from December 2021 to April 2022, with a purposive sampling of 12 PVG developers from nine teams in China. Conventional and directed content analysis was used for data analysis. RESULTS: The interviews identified PVG developers' understanding of PVGs, their current practice experience, and the challenges of developing PVGs. Participants believed PVGs were a type of health education material for patients; therefore, it should be based on patient needs and be understandable and accessible. Participants suggested that PVGs could be translated/adapted from one or several clinical practice guidelines (CPG), or developed de novo (i.e., the creation of an entirely new PVG with its own set of research questions that are independent of existing CPGs). Participants perceived those existing methodological guidelines for PVG development might not provide clear instructions for PVGs developed from multiple CPGs and from de novo development. Challenges to PVG development include (1) a lack of standardized and native guidance on developing PVGs; (2) a lack of standardized guidance on patient engagement; (3) other challenges: no publicly known and trusted platform that could disseminate PVGs; concerns about the conflicting interests with health professionals. CONCLUSIONS AND PRACTICE IMPLICATIONS: Our study suggests clarifying the concept of PVG is the primary task to develop PVGs and carry out related research. There is a need to make PVG developers realize the roles of PVGs, especially in helping decision-making, to maximize the effect of PVG. It is necessary to develop native consensus-based guidance considering developers' perspectives regarding PVGs.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".