Adherence to and Motivations for Complying with Public Health Measures Among Adolescents During the Coronavirus Disease (COVID-19) Pandemic in Canada
Bibliographic record
Abstract
Background: Public health measures (e.g., minimizing social interactions, social distancing, mask wearing) have been implemented in Canada to reduce the transmission of COVID-19. Given that adolescents may be a high-risk demographic for spreading COVID-19, this study investigated adherence to and motivations for complying with public health measures among youth living in Canada at two points of the COVID-19 pandemic. Methods: Adolescents (N = 1484, 53% female, Mage = 15.73 [SD = 1.41]) completed an online survey in either Summer 2020 (W1; n = 809, 56% female) or Winter 2020/2021 (W2; n = 675, 50% female). Independent sample t-tests investigated differences in adherence across waves and regression analyses examined predictors of adherence. Results: Youth engaged in similar levels of social interactions at W1 and W2. Relative to W1, adolescents reported more mask wearing, but less social distancing at W2. At both waves, social responsibility and not wanting to get sick predicted mask wearing, and social responsibility predicted social distancing. Being concerned with population health predicted adherence to all public health measures at W1, whereas concern with family health predicted adherence to mask wearing and social distancing at W2. Conclusions: Youth engaged in more mask wearing but less social distancing as the pandemic progressed. Social responsibility and not wanting to get sick were consistent predictors of adherence throughout the pandemic. Youth shifted from adhering to public health measures due to concern with population health to concern with family health as the pandemic progressed. These results can inform targeted campaigns to bolster compliance with public health measures among adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".