Principalism in public health decision making in the context of the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic lead scientists and governmental authorities to issue clinical and public health recommendations based on progressively emerging evidence and expert opinions and many of these fast-tracked to peer-reviewed publications. Concerns were raised on scientific quality and generalizability of this emerging evidence. MAIN ARGUMENT: However, this way acting is not entirely new and often public health decisions are based on flawed and ambiguous evidence. Thus, to better guide decisions in these circumstances, in this article we argue that there is a need to follow fundamental principles in order to guide best public health practices. We purpose the usefulness of the framework of principalism in public which has been proved useful in real life conditions as a guide in the absence of reliable evidence. CONCLUSIONS: It is recommended the implementation of these principles in an integrated manner adopting an holistic system approach to health policies adapted to specificities of local contexts.
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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 | 0.159 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.108 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.016 |
| 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".