Bibliographic record
Abstract
This dissertation consists of three separate studies with the central focus of better understanding physician prescribing behavior by examining their responses to three different policy interventions using patient level data from China. In the first study, I assess the cost-effectiveness of an antibiotic stewardship program for patients with acute respiratory infections (ARIs) by taking account of physician responses. I employ a net benefit regression approach, which demonstrates that the program has a positive net benefit and that it has the potential to be scaled-up with moderate resources required from the perspective of a publicly funded health care system. The second study investigates the spillover effects of information diffusion on physician prescribing behavior utilizing a difference-in-difference framework. I find that physicians increased their prescribing of Traditional Chinese Medicine (TCM) after learning about the negative consequences of prescribing antibiotics to patients with ARIs. I also find that prescriptions which contain a higher proportion of TCMs are associated with lower total expenditures, lower non-medicine expenditures, lower out-of pocket expenditures but higher medicine expenditures. These findings suggest that information about one type of medicine and its effects can lead to changes in prescribing behavior of other medicines (i.e., those that may not be the direct target of the policy), which in turn, may result in unintended financial implications. The third study investigates how physicians in rural China respond to financial incentives. Faced with the introduction of a policy by the government in 2018 which aimed to increase physician income and increase non-medicine service expenditures as a percentage of total medical expenditure, physicians increased non-medicine expenditure while decreasing medicine expenditure. This change in the expenditure mix aligns with the predicted effects of current financial schemes based on capitated global budgets with a zero-markup for prescription medicines that had been implemented by the government. In addition, physicians decreased medicine expenditures by reducing the value on each type of medicine they prescribed rather than reducing the number of medicines prescribed to patients. These results suggest that physician agency (the physicians maximizing their own financial returns) is an important driving factor of physician prescribing behavior.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".