Conceptual analysis of health systems resilience: A scoping review
Bibliographic record
Abstract
System resilience has long been an area of study, and the term has become increasingly used across different sectors. Studies on resilience in health systems are more recent, multiplying particularly since the 2014 Ebola epidemic in West Africa. The World Health Organization (WHO) is calling for national governments to increase the resilience of their health systems. Concepts help define research objects and guide the analysis. Yet, to be useful, concepts need to be clear and precise. We aimed to improve the conceptual understanding of health systems resilience by conducting a scoping review to describe the state of knowledge in this area. We searched for literature in 10 databases, and analyzed data using a list of themes. We evaluated the clarity and the precision of the concept of health systems resilience using Daigneault & Jacob's three dimensions of a concept: term, sense, and referent. Of the 1091 documents initially identified, 45 met the inclusion criteria. Term: multiple terms are used, switching from one to the other to speak about the same subject. Sense: there is no consensus yet on a unique definition. Referent: the magnitude and nature of events that resilient health systems face differ with context, covering a broad range of situations from sudden crisis to everyday challenges. The lack of clarity in this conceptualization hinders the expansion of knowledge, the creation of reliable analytical tools, and the effectiveness of communication. The current conceptualization of health systems resilience is too scattered to enable the enhancement of this concept with great potential, opening a large avenue for future research.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".