Rural-urban migration patterns and mental health diagnoses of adolescents and young adults in British Columbia, Canada: a case-control study
Bibliographic record
Abstract
BACKGROUND: The identification of mental health problems early in life can increase the well-being of children and youth. Several studies have reported that youth who experience mental health disorders are also at a greater risk of developing psychopathological conditions later in life, suggesting that the ability of researchers and clinicians to identify mental health problems early in life may help prevent adult psychopathology. Using large-scale administrative data, this study examined whether permanent settlement and within-province migration patterns may be linked to mental health diagnoses among adolescents (15 to 19 years old), young adults (20 to 30 years old), and adults (30 years old and older) who grew up in rural or urban communities or migrated between types of community (N = 8,502). METHODS: We conducted a nested case-control study of the impact of rural compared to urban residence and rural-urban provincial migration patterns on diagnosis of mental health. Conditional logistic regression models were run with the following International Classification of Diseases, 9th Revision (ICD-9) mental health diagnoses as the outcomes: neurotic disorders, personality disorder, acute reaction to stress, adjustment reaction, depression, alcohol dependence, and nondependent drug abuse. Analyses were conducted controlling for paternal mental health and sociodemographic characteristics. RESULTS: Mental health diagnoses were selectively associated with stability and migration patterns. Specifically, adolescents and young adults who were born in and grew up in the same rural community were at lower risk of being diagnosed with acute reaction to stress (OR = 0.740) and depression (OR = 0.881) compared to their matched controls who were not born in and did not grow up in the same rural community. Furthermore, adolescents and young adults migrating between rural communities were at lower risk of being diagnosed with adjustment reaction (OR = 0.571) than those not migrating between rural communities. No differences were found for diagnoses of neurotic disorders, personality disorder, alcohol dependence, and nondependent drug abuse. CONCLUSIONS: This study provides some compelling evidence of the protective role of rural environments in the development of specific mental health conditions (i.e., depression, adjustment reaction, and acute reaction to stress) among the children of sawmill workers in Western Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".