System engineering of Singapore long-term care financing and delivery system
Bibliographic record
Abstract
The unique healthcare model of Singapore is formed by the combination of free market principles with careful government control. Singapore has gained extensive international recognition and praises and is ranked 6th in overall health system performance in the world. As Singapore continues to realize its National Health Plan, it has encountered challenges such as a rapid aging population and rising health care cost. The underdevelopment in Singapore long-term care delivery system and a lack of long-term care financing has led to a series of plans and initiatives undertaken by the government to rapidly develop this sector in order to meet the rising demands for long-term care services. Step-down care system was introduced to deliver a seamless care transition from acute hospitals to long-term care facilities to receive more appropriate treatments at a lower cost. Furthermore, financing policies were amended to increase in coverage and subsidies for long-term care services so to relieve the financial burden on the patient’s family. \n \nSoft System Methodology (SSM), a qualitative methodology developed by Peter Checkland will be used to address the complex set of arrangements in the Singapore long-term care system which involves all tiers of government and with care provided by a range of public, charitable, private and community providers. Furthermore, case studies of Australia, Canada and Japan health system will be analyzed to understand their success factors and pitfalls. The insights gained will be applied identify and improve the inefficiencies and inadequacies in the long-term care delivery and financing strategies in Singapore health system. Conceptual models will be developed and compared to the existing health care model to develop recommendations which bridges the reality to the ideal system as proposed through SSM. Hence, a high quality healthcare system with seamless care transition that is cost effective, efficient, affordable and accessible can be achieved.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".