Knowledge mapping of barriers and strategies for clinical practice guideline implementation: a bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: This study provides a comprehensive overview of the knowledge structure and research hotspots regarding barriers and strategies for the implementation of clinical practice guidelines. METHODS: Publications on barriers and strategies for guideline implementation were searched for on Web of Science Core Collection from database inception to October 24, 2022. R package bibliometrix, VOSviewer, and CiteSpace were used to conduct the analysis. RESULTS: The search yielded 21,768 records from 3,975 journals by 99,998 authors from 3,964 institutions in 186 countries between 1983 and 2022. The number of published papers had a roughly increasing trend annually. The United States, the United Kingdom, and Canada contributed the majority of records. The University of Toronto, the University of Washington, and the University of Sydney were the biggest node in their cluster on the collaboration network map. The three journals that published the greatest number of relevant studies were Implementation Science , BMJ Open , and BMC Health Services Research . Grimshaw JM was the author with the most published articles, and was the second most co-cited author. Research hotspots in this field focused on public health and education, evidence-based medicine and quality promotion, diagnosis and treatment, and knowledge translation and barriers. Challenges and barriers, as well as societal impacts and inequalities, are likely to be key directions for future research. CONCLUSIONS: This is the first bibliometric study to comprehensively summarize the research trends of research on barriers and strategies for clinical practice guideline implementation. A better understanding of collaboration patterns and research hotspots may be useful for researchers. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A247.
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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 | 0.046 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.215 | 0.294 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".