Conceptualising centres of excellence: a scoping review of global evidence
Bibliographic record
Abstract
OBJECTIVE: Globally, interest in excellence has grown exponentially, with public and private institutions shifting their attention from meeting targets to achieving excellence. Centres 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 conceptualise CoEs. DESIGN: and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. DATA SOURCES: PubMed, Scopus, CINAHL, Google Scholar and the Google engine until 1 January 2021. ELIGIBILITY: Articles that describe CoE as the main theme. 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 USA, 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 'CoE'-an influential brand to stimulate best practices. We identified 12 essential foundations of CoE-specialised expertise; infrastructure; innovation; high-impact research; quality service; accreditation or standards; leadership; organisational structure; strategy; collaboration and partnership; sustainable funding or financial mechanisms; and entrepreneurship. CONCLUSIONS: CoEs have significant scientific, political, economic and social impacts. However, there are inconsistent use and self-designation of the brand without approval by an independent, external process of evaluation and with high ambiguity between 'CoEs' and the ordinary 'institutions' or 'centres'. 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.
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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 | 0.092 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.051 | 0.042 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| 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".