Anticipated need, demand, and supply of doctors and beds in China: approaching a turning point
Bibliographic record
Abstract
Over the past decade, China has experienced a surge in doctors and beds, along with significant increase in healthcare utilization. This study compares the need-based and demand-based projections with supply of doctors and beds in China from 2021 to 2025. We also evaluate the adequacy and equitable distribution of doctors and beds. By utilizing public data from 2008 to 2018, we employed the health need method, health demand method, and trend projection to estimate the need and demand for and supply of doctors and beds in China. By 2025, the projected need-based, demand-based, and supply of doctors will be 5.93 million, 3.89 million, and 4.40 million, respectively. Correspondingly, bed projections stand at 7.27 million, 6.58 million, and 11.06 million. The study found an inclination toward hospital care, amplified by the need and demand for doctors and beds in hospital sector. Notably, the findings indicate 64.0% and 87.5% of incremental doctors and beds supply will be in hospital sector by 2025, intensifying towards a hospital-centric healthcare system. Considering current trajectories, this study reveals an excess supply of doctors and beds in China by 2025, with widening disparities that favour inefficient hospitals than primary care institutions. We recommend the government to emphasize the importance of optimal investment and allocation of doctors and beds between hospital and primary care sectors, reform the payment method in favour of primary care, and reorient public preferences for hospital services, by strengthening the capacity and quality of primary sector in order to gain population’s trust and confidence.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".