The Neighbourhood Built Environment and Trajectories of Depression Symptom Episodes in Adults: A Latent Class Growth Analysis
Bibliographic record
Abstract
AIM: To investigate the effect of the neighbourhood built environment on trajectories of depression symptom episodes in adults from the general Canadian population. RESEARCH DESIGN AND METHODS: We used 10 years of data collection (2000/01-2010/11) from the Canadian National Population Health Study (n = 7114). Episodes of depression symptoms were identified using the Composite International Diagnostic Interview Short-Form. We assessed the presence of local parks, healthy food stores, fast food restaurants, health services and cultural services using geospatial data. We used latent class growth modelling to identify different trajectories of depression symptom episodes in the sample and tested for the effect of neighbourhood variables on the trajectories over time. RESULTS: We uncovered three distinct trajectories of depression symptom episodes: low prevalence (76.2% of the sample), moderate prevalence (19.2%) and high prevalence of depression symptom episodes (2.8%). The presence of any neighbourhood service (healthy food store, fast-food restaurant, health service, except for cultural service) was significantly associated with a lower probability of a depression symptom episode for those following a trajectory of low prevalence of depression symptom episodes. The presence of a local park was also a significant protective factor in trajectory groups with both low and moderate prevalence of depression symptom episodes. Neighbourhood characteristics did not significantly affect the trajectory of high prevalence of depression symptom episodes. CONCLUSIONS: For individuals following a trajectory of low and moderate prevalence of depression symptom episodes, the neighbourhood built environment was associated with a shift in the trajectory of depression symptom episodes. Future intervention studies are recommended to make policy recommendations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".