Causal Optimization Model for Balanced Allocation of Medical Resources and Analysis of Big Data-Driven Robust Decision Support
Bibliographic record
Abstract
The balanced allocation of medical resources is a core measure to address the issues of "difficulty in accessing medical care and high medical costs" and a key proposition for advancing the Healthy China initiative. Traditional allocation models, which rely on experiential decision-making and correlation analysis, struggle to accurately identify the causal relationship between resource supply and health needs, resulting in insufficient allocation efficiency and fairness. Centering on causal inference and robust optimization theory, combined with the multi-dimensional enabling characteristics of big data technology, this paper systematically reviews the construction logic and core methods of causal optimization models for balanced medical resource allocation, as well as the implementation path of a big data-driven robust decision support system. Following the logical framework of "causal identification - model optimization - decision implementation", the study analyzes the adaptive scenarios of different causal models in resource allocation, explores the application value of big data technology in enhancing decision robustness, and finally points out the current research bottlenecks and future development directions. It aims to provide theoretical references for the scientificization and precision of medical resource allocation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".