Exploring Maternal Birthplace and Child Gender Disparities in Markers of Neglect and Maltreatment among Young Children of Immigrants
Bibliographic record
Abstract
Significant immigration to Canada has brought many cultures and parenting practices together, highlighting differences in child health and wellbeing. Given the increasingly large amount of migration from countries with high gender inequality, it is unknown how parental immigration interacts with child gender to affect healthcare and wellbeing in early childhood. In this dissertation, I present four studies regarding variation in routine preventive health care and maltreatment in very early childhood by maternal birthplace and child gender. The first study is a scoping review where I mapped the use of gender-based analysis in research on the health of children in immigrant families. I found that child gender is an understudied aspect of immigrant children’s health, thereby presenting an opportunity for further research. Next, in three population-based retrospective cohort studies, I compared the risk of three markers of child health care and well-being across immigrant maternal birthplaces in comparison to mothers born in Canada—immunizations, well-child visits at 24 months, and early child maltreatment at five years of age. To explore whether son preference affects child routine preventive care and maltreatment, I also compared outcomes between daughters and sons within families. I demonstrate that children of immigrants are well cared for concerning routine immunizations and are less likely to experience maltreatment in early childhood than children of non-immigrants. Maternal birthplaces associated with high levels of gender inequity do not seem to place daughters at risk of adverse outcomes compared to sons, except for a select case. Results support addressing vaccine hesitancy and child maltreatment in the general population to promote well-being in early childhood, as well as select targeted approaches among specific immigrant groups. My studies provide a model (including data sources, study design, and analytic techniques) to monitor and detect gender inequality in the general population as well as among minority groups. My research adds to the evidence around gender equity, which will hopefully ensure girls continue to achieve the same level of health care and well-being as boys.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".