Probing popular and political discourse on antimicrobial resistance in China
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) is an increasing threat to global public health that is largely exacerbated by the overuse and misuse of antimicrobial medicines. As the largest antimicrobials producer and user in the world, China has a critical role to play in combatting AMR. By examining Chinese news articles and policy statements, we aim to provide an authentic understanding of public discourse in China on AMR. METHODS: A search was conducted using two of the most comprehensive digital libraries for Chinese news media documents. Chinese policy documents were retrieved from official Chinese government websites. Records from June 2016 to May 2017 were included. Grounded theory was used to analyze included records, and we followed an iterative thematic synthesis process to categorize the key themes of each document. RESULTS: Across 64 news articles, most articles delivered general knowledge about AMR and debunked AMR-related myths, explored the implications of AMR-relevant policies, and discussed the misuse of antimicrobials in the agricultural sector. All policy documents provided guidance for healthcare workers, encouraging them to better manage antimicrobial prescriptions and usage. CONCLUSIONS: While the Chinese media actively educates the public on strategies for AMR prevention, certain news articles risk misleading readers by downplaying the hazards of domestic AMR issues. Further, although several national policies are geared towards combatting AMR, the government faces difficult challenges in overcoming public misconceptions regarding antimicrobial use. Records from the regional level should also be examined to further explore China's public discourse on AMR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".