Research on the child healthcare system in China from the perspective of equal health benefits: Beijing as the case study
Bibliographic record
Abstract
BACKGROUND: With government's proposal of the Healthy China Strategy, universal health has become the focus of increasing attention in China. As a special group, children's health is the basis and start of the national health. The construction of children's healthcare system should be given the priority. This study applied the concept of health equal benefits and conducted a systematic and in-depth theoretical and empirical research on the children's healthcare system in Beijing, aiming at proposing relevant countermeasures and suggestions such as constructing the path of children's health care system in China. METHODS: This study adopted the way of questionnaire on children's basic information, health status, health service demand, insured situation and system satisfaction and then analysed the children's health, healthcare, satisfaction influence factors and their healthcare needs based on a survey in Beijing China. Methods such as descriptive statistics, analysis of variance and logistic regression are adopted to analyse the correlation between factors affecting children's health. RESULTS: The findings show that overall health of the children in Beijing was better. However, there are still problems such as insufficient resource coverage and incorrect cognition in some groups. Their mental health and support need to be put emphasis on. The popularization of medical insurance policies for urban and rural residents in Beijing needs to be further improved. The burden of seeing a doctor is slightly lower than before, and the contribution to protecting children's health is not strong enough. CONCLUSIONS: Through the survey of children in Beijing, it can be seen that there are still some problems existing in the healthcare system of children in China from the perspective of equal benefit. This study draws policy attention to the issues of financing, service contents, targets, service level and the degree of social recognition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".