Understanding variations in the trajectories of academic achievement and mental health service utilization of foreign-born adolescents in British Columbia, Canada: A population-based cohort study
Bibliographic record
Abstract
ABSTRACTObjectivesThe aim of the study was to examine variations in the academic achievement and mental health service utilization of foreign-born adolescents in British Columbia (BC), Canada, and to subsequently identify predictors of this variation. The objectives of the study were to (a) characterize the academic achievement and mental health service utilization trajectories of foreign-born adolescents over the course of high school, (b) identify assets and risks predicting their academic achievement and mental health service utilization trajectories, and (c) identify the relationship between academic achievement and mental health service utilization.ApproachLeveraging administrative data from the BC Ministry of Health, BC Ministry of Education, and Citizenship and Immigration Canada, the study looked retrospectively at a population-based cohort of foreign-born adolescents in BC over the course of their high school years (Grades 10-12), in comparison to a random sample of their Canadian-born peers. ResultsUtilizing Group-based Trajectory Modeling, the study identified that foreign-born adolescents in BC followed a range of academic achievement and mental health service utilization paths. By way of multinomial logistic regression, the study subsequently identified a number of assets and risks that helped to explain the odds of membership in each trajectory group. Finally, utilizing dual trajectory modeling, the study found a relationship between academic achievement trajectory group membership, conditional upon mental health service utilization group membership.ConclusionAs expected, a number of assets and risks as well as cumulative assets and risks associated with migration experiences were found to be powerful predictors of the variation in academic achievement and mental health service utilization for foreign-born adolescents in BC. The results support moving away from a one-size-fits-all understanding of the impact of migration on adolescent development. The utility of contextualizing migration experiences to gain a better understand of who is most likely to struggle or succeed is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".