Investigating adolescent psychological wellbeing in an educational context using PISA 2018 Canadian data
Bibliographic record
Abstract
Introduction: Adolescent psychological wellbeing has been identified as an important public health priority and one of the major challenges facing young people. However, few studies have examined the wellbeing of Canadian adolescents nationwide in the past decade, and even fewer have focused specifically on immigrant adolescents. This study aims to investigate Canadian adolescent psychological wellbeing (PWB) via nationally representative data from the Programme for International Student Assessment (PISA) 2018. We explored what social and educational factors were critical in predicting Canadian adolescents' PWB, how adolescents from immigrant families differed from their non-immigrant peers in their wellbeing, and how adolescents' PWB was related to their academic performance. Methods: A total of 22,651 Grade 8 Canadian students who participated PISA 2018 were included in this study (mean age of 15; 49.9% female; 26% immigrant students). Mixed effects modeling was adopted for data analysis. Results: Our results showed that various social and educational factors were associated with adolescent PWB, but these relationships varied depending on which aspect of PWB was examined. Immigrant adolescents were shown to have higher levels of PWB when student attitudes toward immigrants were more positive. Additionally, most aspects of PWB were important for achievement performance. Discussion: It is important to develop healthy and supportive school and disciplinary climates that foster students' sense of belonging. To further support the PWB of immigrant adolescents, educators can implement activities and integrate learning materials on cultural diversity into curricula, encouraging students to develop positive attitudes toward immigrants. Our findings on the PWB of Canadian adolescents could provide valuable insights for other countries with diverse populations, particularly those with significant immigrant communities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".