Operationalizing Systems Thinking to Sustain Public Health Rehabilitation Programs: An Integrative Strategic Synthesis
Bibliographic record
Abstract
Background Sustainability in Public Health Rehabilitation Programs (PHRPs) is essential to ensure the ongoing delivery of equitable, accessible, and high-quality care, especially amid rising demands from aging populations, chronic disease burdens, and resource constraints. However, PHRPs frequently face challenges such as fragmented service delivery, unstable funding, and shifting political priorities, which threaten long-term effectiveness. Systems Thinking (ST)—an approach that emphasizes feedback loops, stakeholder engagement, and leverage points—offers a promising pathway to strengthen the sustainability of these programs. Methods We conducted a narrative review and thematic synthesis of peer-reviewed literature and global health case studies published between January and March 2025. Guided by the World Health Organization’s 10-step Systems Thinking framework and the Systems Thinking for Health (ST4H) model, we analyzed how ST has been operationalized to support the sustainability of PHRPs in various global contexts. Results Three core systems mechanisms—feedback loops, stakeholder engagement, and leverage points—were identified as critical contributors to the sustainability of PHRPs. Case studies from Canada, Brazil, South Africa, the UK, India, and Jordan demonstrated how tools like systems mapping, real-time feedback, and collaborative planning led to tangible improvements such as reduced service duplication, better patient retention, and more efficient care transitions. Thematic synthesis highlighted that ST supports adaptive learning, cross-sector alignment, and strategic resource use. However, challenges included limited systems literacy, weak inter-organizational coordination, and complexity in tool application, particularly in resource-constrained settings. Overall, the degree of sustainability improvement was linked to how deeply ST was integrated into program design and supported by enabling structures such as data infrastructure and leadership engagement. Conclusion Embedding ST into rehabilitation policy and program design can help navigate the complexity of public health systems and foster long-term sustainability. To support scalable and person-centered rehabilitation, increased systems literacy within the health workforce and greater cross-sector collaboration are essential. ST provides a practical, adaptive framework for addressing systemic barriers and enhancing the resilience and effectiveness of PHRPs globally.
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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 | 0.050 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".