Conceptualizing centers of excellence: A global evidence
Bibliographic record
Abstract
Abstract Objective Globally, interest in excellence has grown exponentially, with public and private institutions shifting their attention from meet targets to achieving excellence. Centers of Excellence (CoEs) are standing at the forefront of healthcare, research, and innovations responding to the world’s most complex problems. However, their potential is hindered by conceptual ambiguity. We conducted a global synthesis of the evidence to conceptualize CoEs. Design Scoping review, following Arksey and O’Malley’s framework and methodological enhancement by Levac et al to analyze the evidence and the PRISMA-ScR to guide the retrieval and inclusion of the evidence. Data sources PubMed, Scopus, CINAHL, Google Scholar, and the Google engine from their inception to 01 January 2021. Eligibility Papers that describe CoE as the main theme, which could be defining, theorizing, implementing, or evaluating a CoE. Results The search resulted in 52,161 potential publications, with 78 articles met the eligibility criteria. The 78 articles were from 33 countries, of which 35 were from the United States of America, 3 each from Nigeria, South Africa, Spain, and India, and 2 each from Ethiopia, Canada, Russia, Colombia, Sweden, Greece, and Peru. The rest 17 were from various countries. The articles involved six thematic areas - healthcare, education, research, industry, information technology, and general concepts on CoE. The analysis documented success stories of using the brand “Center of Excellence” - an influential brand to stimulate technical skills, innovation, and technology. We identified 12 essential foundations of CoE - specialized expertise; infrastructure; innovation; high-impact research; quality service; accreditation or standards; leadership; organizational structure; strategy; collaboration and partnership; sustainable funding or financial mechanisms; and entrepreneurship. Conclusions CoEs have significant scientific, political, economic, and social impacts. A comprehensive framework is needed to guide and inspire an institution as a CoE and to help government and funding institutions shape and oversee CoEs. Strengths and limitations of this study - To the best of our knowledge, this is the first scoping review to conceptualize centers of excellence based on global evidence. - The study followed Arksey and O’Malley’s framework and methodological enhancement by Levac et al to and the PRISMA-ScR methodological frameworks. - Five databases were systematically searched to identify scientific and gray literature - The study was limited by language restrictions.
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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 | 0.108 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".