A Social Determinants Perspective on Adolescent Mental Health during the COVID-19 Pandemic
Bibliographic record
Abstract
As a framework for understanding the structural factors that affect health, the social determinants of health (SDoH) have particular significance during the developmental stage of adolescence. When the global coronavirus pandemic (COVID-19) began, public health measures (PHMs) implemented to curb its spread shifted adolescents’ daily lives and routines, initiating changes to their mental health. The purpose of this study was to apply the SDoH to investigating the impacts of the pandemic-related PHMs on the mental health of adolescents in Canada. Using a youth engagement approach, interviews were conducted with 33 adolescents aged 14–19 years from two sites in Alberta, Canada. Participants shared their experiences of adjusting to the PHMs and how these shaped their mental health. Findings indicate that PHMs particularly affected the social determinants of education, access to health services, employment and income security, and social support amongst adolescents as online schooling, loss of connection with peers, income instability, and limited health services affected their mental health. Most commonly, adolescents expressed feeling greater anxiety, depression, or loneliness as the SDoH shifted with the PHMs. As we continue to understand the mental health impacts of the pandemic, the SDoH framework can be used to identify salient social determinants and evaluate these determinants post-pandemic. This study draws attention to the need for policies and programs that protect access to key SDoH at such a critical life stage as adolescence and promote their mental health resilience in shifting SDoH contexts.
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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 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".