Mental Health of Children & Youth in Canada
Bibliographic record
Abstract
Do the experiences that children and youth face early in life impact their mental health differently across immigrant generations? And do these associations vary by ethnicity and gender? My thesis addresses these significant and critical questions, as immigrants have been a large contributor to Canada’s population and are expected to grow in the next couple of decades. Research on the complexities of the social environments that shape and contribute to a child and youth’s mental health has been well documented in literature throughout the years. However, current research on immigrant children and youth that have examined the healthy immigrant effect across immigrant generations (immigrants versus. Native-born) in Canada have been sparse as there have been relatively few studies on this topic. Moreover, the studies on the healthy immigrant effect of children and youth immigrants in Canada have been inconclusive if immigrant children and youth have this initial health advantage. My study contributes to the understanding of how children and youth can experience similar life events (e.g., having parent-child educational aspiration discrepancies and sense of community belonging) but can impact their mental health differently based on immigrant status as well as ethnicity and gender. I use data from the Hamilton Youth Study (HYS) (2013) that has a representative sample of first-, second-, and native-born children of children and youth living in Hamilton, Ontario, Canada to examine parent-child educational aspirations and mental health across immigrant generations along with gender. I also use data of the Canadian Community Health Survey-Mental Health (2012) along with the Census data to examine the association of how South Asian and Chinese youth living in similar ethnic neighborhoods contributes to their sense of belonging and impacts their mental health across generations. To test out these associations, I conducted Ordinary Least Squares regression for chapters 2 and 4 and Hierarchal Linear Modelling (HLM) techniques for chapter 3. These three papers contribute to the discussion of the healthy immigrant effect of children and youth by suggesting that children and youth that experience the same events earlier in life can impact their life to a greater extent more than others based on immigrant status, as well as ethnicity and gender.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".