Connections matter: Adolescent social connectedness profiles and mental well‐being over time
Bibliographic record
Abstract
INTRODUCTION: This study examined profiles of social connectedness among early adolescents in grade 7 before the COVID-19 pandemic was declared (Winter 2020), and in grade 8 during the second Wave of the pandemic (Winter 2021). METHOD: Linked data from 1753 early adolescents (49% female) from British Columbia, Canada who completed the Middle Years Development Instrument survey in grades 7 and 8 were used. Participants reported on life satisfaction, depressive symptoms, and connectedness with peers and adults at home, school and in the community. We used Latent Profile Analysis to identify connectedness profiles at both time points, and Latent Transition Analysis to examine transitions in connectedness profiles over time. Multiple regression analyses examined the associations between profile membership in grade 7 and mental well-being in grade 8, and the associations between transitions in profile membership (i.e., increase vs. decrease in connectedness over time) and mental well-being. RESULTS: Connectedness in multiple domains in grade 7 was related to significantly higher levels of mental well-being in grade 8, controlling for demographics, well-being in grade 7, and COVID-related mental health worries. Well-being was highest when students felt highly connected in all domains and lowest when they felt lower levels of connection. Increases in connectedness were associated with improvements in mental well-being and decreases with a decline in well-being over time. CONCLUSIONS: Experiencing connectedness with peers and adults is critical for the mental well-being in early adolescence. Providing opportunities to connect is important in the context of major societal challenges such as the COVID-19 pandemic.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".