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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".