Adolescents’ adoption of COVID-19 preventive measures during the first months of the pandemic: what led to early adoption?
Bibliographic record
Abstract
INTRODUCTION: The objectives of this study were to explore the extent to which adolescents adopted COVID-19 preventive measures in the first few months of the pandemic and to understand their adoption by looking at interconnected adoption-related factors and determining the strength of these factors, particularly among subgroups not expected to be early adopters. METHODS: Analyses focus on data collected during Spring 2020 from 29 eastern Quebec secondary schools that participated in the COMPASS study. Participants (n = 6052) self-reported their knowledge, perception of risk and preventive practices to do with the COVID-19 pandemic. Data were analyzed using structural equation models based on gender and anxiety level. RESULTS: The majority of respondents reported adopting the recommended COVID-19 preventive measures. The results showed three paths leading to adolescents' adoption of these measures: pandemic knowledge; perception of risk related to COVID-19; and, in particular, discussions with relatives about preventive measures and what to do in case of infection. CONCLUSIONS: While most of the adolescent participants in this study appeared to comply with COVID-19 preventive measures, factors such as discussions with relatives emerge as elements to foster in order to improve adolescents' adoption of preventive measures.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".