Unintended Consequences of COVID-19: The Rise of Anti-Asian Violence and Integrative Medicine
Bibliographic record
Abstract
Recent globally important events have accelerated the need to redefine ideas of health, healing and well-being. The COVID-19 pandemic has highlighted the fragility of socio-economic and health care systems, questioning the hegemony of the Global North in addressing global health issues. In times of global interconnectedness, postcolonial dynamics and calls for integrative medicine to address complex health issues that cannot be effectively managed by a single biomedical framework, this review article aims to foster dialogues across multidisciplinary perspectives that engage in questions of health and well-being. By focusing on unintended consequences of COVID-19, specifically regarding anti-Asian violence and the important role of traditional medicines in contributing to an integrative medicine that enhances global health care systems, this article endeavours a deeper theoretical understanding of why certain issues exist as they do, and how they occur, which can provide the basis for predicting their (re)occurrence and for informing meaningful intervention efforts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".