Conceptualizing clinical expertise in evidence-based practice: a narrative literature review with implications for clinical decision-making.
Bibliographic record
Abstract
Objective: This review aimed to explore clinical expertise within evidence-based practice (EBP) by examining contemporary definitions of clinical expertise, how it can be acquired and developed over time, and its role within EBP. Methods: PubMed, Web of Science, and Scopus databases were searched for literature on clinical expertise published between January 2016 and August 2024. Titles and abstracts were screened for relevance. Full-text review was conducted for papers deemed potentially relevant. Results: 23 articles were included in this review. Clinical expertise receives different treatments across literature. However, a commonality is that clinical expertise requires proficiency, skill, and clinical judgement that can be acquired only through clinical experience, collaboration, and hands-on clinical practice. Operating within Haynes' model of EBP, clinical expertise is central to integrating patient preferences and bridging the gap between standardized objective evidence and personalized care. Conclusions: Clinical expertise represents the core of integrating EBP to inform clinical decision-making and is developed through experience and keeping current with research. Author’s Note: This paper is one of seven in a series exploring contemporary perspectives on the application of the evidence-based framework in chiropractic care. The Evidence Based Chiropractic Care (EBCC) initiative aims to support chiropractors in their delivery of optimal patient-centred care. We encourage readers to review all papers in the series.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".