Development, Application and Effectiveness of Clinical Guidelines in General Practice
Bibliographic record
Abstract
Background Evidence-based clinical guidelines are important resources for promoting the provision of high-quality and effective general practice services. Research on the development, application and effectiveness evaluation of guidelines in general practice is insufficient. Objective To understand the development and application of international guidelines in general practice, informing the development and utilization of relevant guidelines in China. Methods The PubMed database was systematically searched from August to September 2022 for studies in English related to the development, application and effectiveness of guidelines in general practice published during January 1, 2012 to September 7, 2022. The author information and focused issues as well as publication journals of the studies were extracted. And associated factors of the development, distribution and use, and effectiveness of guidelines were reviewed. Results (1) A number of countries, including China, conducted the research using quantitative, qualitative, literature review and theoretical methods. (2) Organisations of general practitioners (GPs) and researchers in general practice leading or participating in guideline development, can improve the guideline acceptability and applicability, and the integration of physician experiences and patient preferences in shaping the guideline. General practice consensus is an appropriate type of the guideline. Referral recommendation is one key component of the guidelines. Guideline analysis and adaptation can be used as a strategy for GPs training. (3) Compared with specialty guidelines, the application of guidelines in general practice has been affected by more factors. Problem-oriented and self-guided guideline trainings are beneficial to the improvement of guideline compliance. Clinical audits and evidence-based medicine journal clubs are common and effective approaches for promoting guideline implementation. GPs' feedback on the guideline contributes to continuous improvement of the guidelines. (4) The impact of these guidelines on GPs' practice behaviour can be assessed observationally, while that on patient outcomes requires to be assessed using more rigorous and complex study designs. Conclusion GPs' participation in the development, implementation and evaluation of the guidelines is an enviable trend of the development in evidence-based general practice. GPs' experience and humanism, patients' preferences and expectations, and various internal and external factors associated with general practice, all need to be incorporated into the development, implementation, and evaluation of the guidelines, under collaboration with methodological experts.
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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 | 0.088 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".