Policy responses to COVID-19 present a window of opportunity for a paradigm shift in global health policy: An application of the Multiple Streams Framework as a heuristic
Bibliographic record
Abstract
Drawing on Kingdon's Multiple Streams Framework as a heuristic, this article reviews the three streams - problems, policies, and politics - as applied to the adoption of economic policies in response to the socioeconomic impacts of COVID-19. In doing so, we argue that we are currently presented with a window of opportunity to better address the social determinants of health. First, through assessing the problem stream, an understanding of inequity as a problem gained wider recognition through the disproportionate impacts of COVID-19. Second, in the policy stream, we demonstrate that appropriate and unprecedented policies can be enacted even in the face of changing evidence or evidentiary uncertainty, which are needed to address upstream factors that influence health. Lastly, in the politics stream, we demonstrate that addressing a public health 'problem' can be well-received by the public, making it politically viable. However, it is important to ensure the 'problem' is clearly relayed to the public and that this information is not perceived to change, as this can undermine trust. The social, political, and behavioural lessons presented by the COVID-19 pandemic should be drawn on in this pivotal moment for global public health.
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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 | 0.047 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".