‘Staying in the lane’ of public health? Boundary‐work in the roles of state health officials and experts in COVID‐19 policymaking
Bibliographic record
Abstract
The state-level COVID-19 response in the United States necessitated collaboration between governor' offices, health departments and numerous other departments and outside experts. To gain insight into how health officials and experts contributed to advising on COVID-19 policies, we conducted semi-structured interviews with 25 individuals with a health specialisation who were involved in COVID-19 policymaking, taking place between February and December 2022. We found two diverging understandings of the role of health officials and experts in COVID-19 policymaking: the role of 'staying in the lane' of public health in terms of the information that they collected, their advocacy for policies and their area of expertise and the role of engaging in the balancing of multiple considerations, such as public health, feasibility and competing objectives (such as the economy) in the crafting of pandemic policy. We draw on the concept of boundary-work to examine how these roles were constructed. We conclude by considering the appropriateness as well as the ethical implications of these two approaches to public health policymaking.
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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 | 0.111 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.038 | 0.149 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".