Feeling safe: a critical look at the effect of neighborhood safety features and perceptions on childhood symptoms of depression
Bibliographic record
Abstract
BACKGROUND: Physical characteristics and perceptions of an environment can have enduring effects on one's mental health. The present study aimed to determine whether a set of measures of neighborhood safety - independent built environment features, parents' perception of neighborhood safety and community cohesion, and children's perception of neighborhood safety - best related to symptoms of depression in young boys and girls from Montréal, Québec. METHODS: Data were from the Quebec Adipose and Lifestyle Investigation in Youth (QUALITY) cohort. Participants were aged 8 to 10 years (M = 9.5) at baseline (2005). Measures included: child symptoms of depression, neighborhood perception (child and parentally reported), and independent rater assessed visible disorder/neighborhood safety issues and road safety features. Multiple linear regressions were used to examine the relation between neighborhood safety measures and symptoms of depression for boys (n = 262) and girls (n = 212) at baseline and at follow-up time approximately 2 years later in 2008 after adjusting for baseline sex, age, body mass index, pubertal status, physical activity, family income, parent education, population density, and land-use mix. Analyses were sex stratified. RESULTS: Greater child perceived safety was associated with lower symptoms of depression in boys at both baseline and follow-up, and greater community cohesion was associated with lower symptoms of depression in girls at baseline. These results were not maintained after adjusting for multiple testing. CONCLUSION: Future research should elucidate the more precise pathways linking neighborhood safety to symptoms of depression among children and consider differences across sexes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".