Table2_Greenness moderates the relationship between self-rated social standing and depression among older adults in the Canadian longitudinal study on aging.docx
Bibliographic record
Abstract
Introduction Greenness is considered to be a health-promoting feature of both natural and built environments and has the potential to influence mental health outcomes. However, most studies to date have neglected to address whether greenness differentially affects mental health outcomes for individuals across the socioeconomic spectrum. Our study explored if greenness is a moderating factor in the relationship between socioeconomic status (SES) and mental health using data from the Canadian Longitudinal Study on Aging (CLSA). Methods A cross-sectional design was used to compare mental health outcomes between individuals with different levels of SES and residential greenness. We used self-rated social standing as a measure of SES and depression score measured using the Centre for Epidemiologic Studies 10-Item Depression Scale (CESD-10) as a measure of mental health. Greenness was measured using the annual maximum Normalized Difference Vegetation Index (NDVI) within a 1,000 m buffer area of participants' residential postal code locations. Results There was a statistically significant moderating effect of greenness for the relationship between self-rated social standing and depression score. As greenness increased, individuals with lower self-rated social standing had the greatest decreases in depression score. Discussion The results of our study suggests that targeting greening interventions at individuals and communities with low SES may reduce depressive symptoms overall, as well as decrease socioeconomic inequalities in depression.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".