Adolescent well-being in time of crisis: The role of social and residential contexts
Bibliographic record
Abstract
Abstract Background It is unknown whether social or residential context during the COVID-19 lockdowns may impact mental well-being. We identified classes of each of social and residential context during the lockdown and examined their associations with mental well-being among French adolescents one-year after the first lockdown. Methods We used data collected in a cross-sectional study design in 2021 from 387 adolescents ages 12 to 15 years enrolled in the school-based pilot study for EXIST. Participants reported social and residential characteristics during the lockdown retrospectively, and current mental well-being in self-report questionnaires. We used latent class analysis to identify classes of social and residential contexts, and linear regression models to study their association with mental well-being. Results Four social context classes were identified: class 1 ‘Low opportunities for social contacts at home’, class 2 ‘Moderate opportunities for social contact at home”, class 3 ‘High opportunities for social contact at home’, and class 4 ‘Very high opportunities for social contact at home’. Compared to class 4, lower levels of mental well-being were observed among adolescents in class 1 (b= -4.08, 95% CI [-8.06; -0.10]) one year after the lockdown. We identified four residential context classes, with differences across classes in proximity to nature, type of residence (e.g., apartment, house), and level of neighborhood deprivation. No association was found between residential context during the lockdown and adolescent mental well-being one year later. Conclusions An unsupportive social environment during the lockdown may have long-term effects on mental well-being among adolescents. Given the observed disparities in social support, future intervention should aim to strengthen social support for adolescents living in unsupportive environments (e.g., remote homework assistance, support for isolated parents, provision of virtual chat rooms for adolescents). Key messages • Mental well-being was challenged among youth with lower levels of social connectedness and support during lockdown, residential context was not associated with adolescent well-being after one year. • During crisis, a critical need among adolescents does not pertain to the physical attributes of their living environment, but rather to social support from friends or family within and outside home.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".