Preventing and Appeasing COVID-19 Vaccine Tension in Schools to Protect the Well-Being of Children and Adolescents in Québec, Canada
Bibliographic record
Abstract
Objectives: This article describes an intervention that took place in Québec, Canada, to mitigate COVID-19 vaccine tension in schools, exacerbated by the 12-17 years old vaccination campaign. Building on this initiative, it proposes guiding principles for prevention and intervention in conflict around COVID-19 vaccination in and around schools. Intervention: Three complementary tools were developed by a community program, CoVivre, in collaboration with an interdisciplinary team, to help practitioners and parents understand vaccine tensions and their impact on youth, and to suggest simple ways to prevent and intervene in vaccine related conflicts. Recommendations: A thorough research evaluation could not be performed due to the rapid crisis response; however, the tools received positive feedback by practitioners, institutions, and decision makers. Recommendations were structured around the following principles: (a) fostering transparent and nuanced health communications; (b) avoiding confrontation and refusing to escalate while strongly condemning criminal acts; (c) encouraging open dialogue; and (d) preserving relationships. Implications: Mental health consequences of public health interventions should be considered at inception to avoid collateral damages. Removing children from the heart of societal conflict and maintaining the family-school relationship is crucial to child development. It is imperative to engage interdisciplinary teams to protect youth from societal polarization, and provide an opportunity for growth and resilience. This initiative suggests that more research is needed on the impacts of encouraging an open dialogue around vaccination, and adopting an empathetic approach amongst youth towards others who may not share the same opinion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".