Neighbourhood characteristics related to mental health service use among adults with diabetes: a population-based cohort study in New Brunswick, Canada
Bibliographic record
Abstract
OBJECTIVE: It has been postulated that social and economic inequalities may shape the distributions of comorbid diabetes and mental illness. This observational cohort study using linked population-based administrative and geospatial datasets aimed to describe associations between neighbourhood socioenvironments and disorder-specific mental health service use among adults with diabetes in the province of New Brunswick, Canada. RESULTS: A baseline cohort of 66,275 persons aged 19 and over living with diabetes was identified. One-quarter (26.3%) had used healthcare services for mood and anxiety disorders at least once during the six-year follow-up period 2012/2013-2017/2018. Based on Cox proportional hazards models, the risk of mental health service contacts was significantly higher among those residing in the most materially deprived neighbourhoods [HR: 1.07 (95% CI: 1.01-1.14)] compared to those in the least so, and those in areas characterized with the highest residential instability [HR: 1.13 (95% CI: 1.05-1.22)] compared to those in areas with the lowest instability. Among adults with incident diabetes (N = 4410), age and sex but not neighbourhood factors were related to differential help-seeking behaviours for mental health problems. These findings underscored the gap between theoretical postulations and population-based observations in delineating the syndemics of neighbourhood socioenvironments and mental health outcomes in populations with high diabetes prevalence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".