Разходи за здравеопазване в условия на криза
Bibliographic record
Abstract
Разходването на финансовия ресурс в публичния сектор е сериозно предизвикателство за всяко правителство. Съществена част от него са разходите за здравеопазване. Здравеопазването и образованието са двата стълба, стоящи в ос- новата на съвременния просперитет. От средата на 20-и век разходите за здравна помощ нарастват постоянно. Интерес представлява дали тази тенденция се е запазила и след началото на кризата от 2007-2008 г. Анализирани са разходите за здравни услуги на страните от Г-7 и няколко страни извън Г-7. Обзорът показа, че въпреки затрудненията, които изпитват, всички държави увеличават своите разходи за здравеопазване. Това показва осъзнаване на важността и необходимостта от развитието на сектора. С най-добри показатели са САЩ, Канада и Япония, а с най- влошени Мексико, Китай и Индия. Въпреки големите си претенции страни като Китай и Индия си остават бедни и недоразвити, факт подкрепян от най-показателния индикатор като БВП на глава от населението Healthcare Expenditure in Times of Crisis Spending of financial resources in the public sector is a major challenge for any government. Mainly part of it is a healthcare expenditure. Health and education are the base of today’s prosperity. The cost of healthcare is rising permanently since the middle 20th century. The question is whether this trend has been continued after the crisis of 2007-2008. We analyzed the cost of health services between the G-7 and several developing countries. The review showed that all countries increase their health care costs despite. Countries like USA, Canada and Japan have good results whereas Mexico, China and India are with the worst indicators. Despite the ambitions and claims for progress China and India remain poor and undeveloped, a fact supported by most significant indicator such as GDP per capita.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.042 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.108 |
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; both teacher heads agree on what is shown here.
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".