Status of the economy and key challenges faced by the healthcare sector in Sri Lanka
Bibliographic record
Abstract
Ceylon College of Physicians (CCP) chose the theme of ‘Holistic care amidst constraints’ for the Annual Academic Sessions 2023. As the Chief Guest, I was invited to present an independent economist’s perspective on the current status of the economy and to share some thoughts on two key challenges currently faced by the healthcare sector in Sri Lanka, namely the brain drain and the funding of healthcare. The first part of this paper sets out the key contributory factors that caused the current economic crisis and the policy measures taken at present to reverse these to stimulate a sustainable economic recovery. The success of these reforms so far based on key economic indicators such as exchange rate, interest rates, inflation, GDP growth, foreign exchange reserves, government revenue, trade deficit, worker remittances and tourism earnings, are examined thereafter. Areas of concern which require attention to ensure sustainability of the process and to ensure that nobody is left behind, are highlighted.Causes and remedies of brain drain are discussed, looking beyond those which look obvious but are currently unfeasible due to the state of the economy. The pace of emigration needs to be slowed while adapting to the challenges posed by this inevitable phenomenon and learning to live with it. Regarding funding of healthcare, issues faced by the current two-tiered system in Sri Lanka are examined, particularly the high level of out-of-pocket spending.Different models adopted by countries such as the UK, Canada, France, Germany, Japan, and the USA are looked at. A model for Sri Lanka to consider adopting is proposed to help reach the goal of Universal Health Coverage and an attempt is made to chart a path to fulfil this vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".