The role of green and blue spaces in perinatal maternal mental health outcomes during the transition to parenthood
Bibliographic record
Abstract
INTRODUCTION: Green spaces and blue spaces associate positively with mental health outcomes. However, research on their effects within the perinatal population is limited. The unique needs and circumstances of this group and their increased risk for poor mental health underscore the importance of understanding their relationship with their immediate physical environments. The current study investigates how proximity to green spaces and blue spaces relate to perinatal depression and anxiety symptoms and if the strength of this relationship varies over the perinatal period (prenatal, and 3, 6, 12, & 24 months postpartum). METHODS: Green (NDVI, tree canopy) and blue space (distance from the nearest water body) measures from the Canadian Urban Environmental Health Research Consortium were linked to depression (Edinburgh Postnatal Depression Scale) and anxiety (Patient-Reported Outcomes Measurement Information System) data from the pan-Canadian Pregnancy during the COVID-19 Pandemic (PdP; n = 10,866) cohort study. RESULTS: Greater NDVI (-0.91 (-1.77, -0.05) and shorter distance to water bodies (-0.14 (-0.24, -0.03)) were associated with fewer perinatal depression symptoms. On probing interactions with time, the relationship between tree canopy and NDVI varied over time for both depression and anxiety symptoms, with strongest associations at the prenatal time point. Effects of distance to water bodies did not vary over time. CONCLUSION: Green spaces and blue spaces were associated with fewer perinatal depression and anxiety symptoms, particularly during pregnancy. These findings suggest the need for studies to determine if exposure to green and blue spaces can contribute to reduced depression and anxiety symptoms in expectant individuals.
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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".