Bibliographic record
Abstract
Although the role of competition in the health care sector has been extensively explored in developed countries, evidence from China is limited. This dissertation comprised three research projects that examined the health consequences of competition in the health care industry in China: 1) to explore the impacts of pharmacy competition on drug expenditures by individuals who purchased drugs for influenza at pharmacies from 2015 to 2019 in Changde city, Hunan province, China; 2) to assess the impacts of hospital competition on the quality of outpatient care for individuals who had outpatient visits at hospitals for influenza from 2015 to 2019 in Changde city, Hunan province, China; and 3) to compare the impacts of hospital competition by hospital-type on the quality of inpatient care for individuals who were admitted for chronic obstructive pulmonary disease at hospitals in the fourth quarter of 2017 and 2019 in Sichuan province, China. This dissertation demonstrated that: 1) pharmacy competition was associated with a decline in annual average influenza-specific drug expenditures by individuals with influenza; 2) hospital competition contributed to an increase in the quality of outpatient care for outpatients with influenza; and 3) the impacts of hospital competition on the quality of inpatient care for those with chronic obstructive pulmonary disease depended on the quality measure used and on hospital-type. These findings jointly have implications for the design of health policies and efforts to both enhance the quality of healthcare services and to control healthcare expenditures.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".