Academic activism on behalf of children during the COVID-19 pandemic in Israel; beyond public health advocacy
Bibliographic record
Abstract
Among the challenges presented by the SARS-CoV2 pandemic are those related to balancing societal priorities with averting threats to population health. In this exceptional context a group of Israeli physicians and public health scholars (multidisciplinary academic group on children and coronavirus [MACC]) coalesced, examining the role of children in viral transmission and assessing the necessity and consequences of restricted in-class education. Combining critical appraisal and analytical skills with public health experience, MACC advocated for safe and monitored school re-opening, stressing the importance of education as a determinant of health, continuously weighing this stance against evolving COVID-19-risk data. MACC's activities included offering research-based advice to government agencies including Ministries of Health, Finance, and Education. In a setting where government bodies were faced with providing practical solutions to both decreasing disease transmission and maintaining society's vital activities, and various advisors presented decision-makers with disparate views, MACC contributed epidemiological, clinical and health policy expertise to the debate regarding school closure as a pandemic control measure, and adaptations required for safe re-opening. In this paper, we describe the evolution, activities, policy inputs and media profile of MACC, and discuss the role of academics in advocacy and activism in the midst of an unprecedented public health crisis. A general lesson learned is that academics, based on the rigor of their scientific work and their perceived objectivity, can and should be mobilized to pursue and promote policies based on shared societal values as well as empiric data, even when considerable uncertainty exists about the appropriate course of action. Mechanisms should be in place to open channels to multidisciplinary academic groups and bring their input to bear on decision-making.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".