From Structure to Outcome: The Role of the Donabedian Model in Global Health Service Management Research (1987–2024)
Bibliographic record
Abstract
The Donabedian model—first introduced in 1966—remains the primary foundation for evaluating the quality of health care through three components: structure, process, and outcomes. Although its application has expanded globally, comprehensive mapping of its scientific evolution is still limited. This study aims to explore research trends, conceptual developments, and scientific collaboration networks related to the Donabedian model in healthcare management using bibliometric and scientometric approaches. Data were collected from the Scopus database (1987–2024) and analyzed using BiblioShiny for descriptive exploration, VOSviewer for visualization of co-ocracy networks, and CiteSpace for detecting citation bursts and thematic evolution. The results show a surge in publications since 2015 influenced by value-based service reforms and the global health agenda. Developed countries such as the United States, Canada, and Australia dominate publications, accompanied by increasingly active contributions from low- and middle-income countries such as Ethiopia, reflecting a more inclusive flow of knowledge exchange. The CiteSpace analysis identified 11 thematic clusters, ranging from neonatal care to system transformation and palliative services, demonstrating the conceptual maturity and multidisciplinary relevance of these models. Seminal articles such as Donabedian (1988, 2003) and Crossing the Quality Chasm (2001) occupy a central position in the co-citation network, connecting various research domains. Overall, the study confirms that the Donabedian model has evolved into a cross-contextually adaptive scientific meta-framework and a strategic instrument in equitable and contextual health system reform.
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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 | 0.056 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".