A breach in the social contract: Limited participation and limited evidence in COVID‐19 responses
Bibliographic record
Abstract
Medically trained health professionals have been central to the development of policy responses to the coronavirus 2019 (COVID-19) crisis. In their multiple roles-as clinicians, public health leaders, members of scientific advisory boards, and also as media pundits and health professionals-they have helped shape discourses of science-based policy options during the first 2 years of the pandemic. In particular, health professionals as a collective voice insisted on the necessity of society-wide measures of social control to curb the morbidity and mortality of the virus. These measures, in turn, informed the political and moral imagination of the political class, the media and the larger public. Yet, as emerging evidence suggests, measures of social control posed a serious and long-term risk for health equity. In this discussion piece on the first 2 years of COVID-related public health directives, we interrogate the tensions that advocating for extensive and protracted measures of social control can pose to the social contract between medicine and society, health equity and democracy. To illustrate these tensions, we discuss the public fallout between vocal members of the OSAT, an ad hoc biomedical-led organization, and the Government of Ontario in light of the disagreement on the scope of 'stay home' orders to manage the third wave of the pandemic in the Spring of 2021 and, more recently, the mass protest against mass-scale public health measures in Ottawa, Canada. We argue that while decision making under emergency conditions is a difficult task, the legitimacy of the social contract between medicine and society depends on medical experts' judicious exercise of public health ethics principles. We offer a set of recommendations for building a more collaborative response to future health crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.461 | 0.638 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.023 | 0.050 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".