Operationalizing systems thinking approach to sustain public health rehabilitation programs: a rapid review and strategic synthesis
Bibliographic record
Abstract
Background: Public Health Rehabilitation Programs (PHRPs) are essential to achieving universal health coverage and disability-inclusive health systems. Despite their importance, sustainability is threatened by demographic pressures, funding variability, and weak system integration. Systems Thinking (ST) provides a structured paradigm to address complexity, identify key leverage points, and embed adaptive capabilities for longer-term program survival. Our aim was to summarise global applications of ST in PHRPs and identify mechanisms that most effectively contribute to sustainability. Methods: We conducted a rapid review of peer-reviewed literature and global case studies published between 2010 and 2025. The short timeframe was intentionally selected to provide timely, policy-relevant insights while laying the groundwork for more extensive future reviews. Searches in PubMed, Scopus, and WHO repositories identified studies applying ST to sustain PHRPs. Data were thematically synthesized using the WHO 10-step ST framework and the Systems Thinking for Health (ST4H) model. Results: Six studies from six countries were included. Three mechanisms emerged: (1) Feedback Loops & Adaptive Learning, (2) Stakeholder Engagement & Systems Mapping, and (3) Strategic Leverage Points. Examples from diverse contexts, especially low- and middle-income countries such as Brazil, India, South Africa, and Jordan, demonstrated improved service integration, resilience, and reach. Conclusion: ST offers a robust framework for addressing persistent sustainability challenges in PHRPs. Embedding ST early in program design, supported by cross-sector engagement, systems literacy, and strong governance, enhances adaptability, equity, and efficiency. This rapid review provides actionable evidence for policymakers and practitioners, while also underscoring the need for context-specific sustainability metrics and broader scoping or systematic reviews to deepen and expand the evidence base.
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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 | 0.051 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".