The Privatization of Healthcare in selected First-world Countries and its Implications in the Kingdom of Saudi Arabia: A Descriptive-Comparative Study
Bibliographic record
Abstract
Background: A long-term debate persists in global health concerns about the proper balance in the delivery of healthcare services between the private and public sector to the people of the low and middle income countries. The private sector allies claim that private companies may be more efficient and receptive in fulfilling patient needs, while the public sector allies have stressed the incapacity of the poor people to pay that causes imbalances in access to health care. Aim: This study aims to compare the health-care privatization models of the United Kingdom, Germany, and Canada through health expenditure statistics. The findings will serve as a source of benefits from the privatization of health sectors in Kingdom of Saudi Arabia to strengthen its implementation. Method: This is a descriptive-comparative research where the healthcare privatization model among the chosen first-world countries will be compared through the available health expenditure statistics. Results: The United States of America (USA) has the highest Total Health Expenditure as a percentage of the Gross Domestic Product with 15-17% since 2006, and lasted until 2014. Saudi Arabia has only 3-5% as compared to the selected first-world countries. The United Kingdom (UK), Germany, Canada, and Saudi Arabia have significantly higher General Government Health Expenditure as a percentage of Total Health Expenditure. On the contrary, the Private Health Expenditure (PvtHE) of USA is higher with 52-55% than the GGHE with only 45-48%. Also, the private insurance as a percentage of PvTHE of the USA is the highest with 63-65% as compared to UK, Germany, Canada and Saudi Arabia. Conclusion: The healthcare system of Saudi Arabia is a mainly public-funded and public-owned similar to the UK, Germany, and Canada. Despite of the effectiveness of the public-contract model, the private insurance or provider model and the privatization of hospitals could notably change the healthcare system in Saudi Arabia. Thus, the resources and the quality of healthcare services in Saudi Arabia must be enhanced through these mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".