Study of active neighborhoods in Detroit (StAND): study protocol for a natural experiment evaluating the health benefits of ecological restoration of parks
Bibliographic record
Abstract
BACKGROUND: Individuals living in deprived inner cities have disproportionately high rates of cancers, Type 2 diabetes and obesity, which have stress- and physical inactivity-related etiologies. This study aims to quantify effects of ecological park restoration on physical activity, stress and cardio-metabolic health outcomes. METHODS: The Study of Active Neighborhoods in Detroit is a quasi-experimental, longitudinal panel natural experiment with two conditions (restored park intervention (INT) and control (CNT)) and annual measurements at baseline and 3-years post-restoration. Individuals (sampled within 500 m of an INT/CNT park) serve as the unit of analysis. Restoration (n = 4 parks) involves replacing non-native plants and turf with native plants; creating trails; posting signage; and leading community stewardship events. The CNT condition (n = 5) is an unmaintained park, matched to INT based on specified neighborhood conditions. Recruitment involves several avenues, with a retention goal of 450 participants. Park measures include plant/avian diversity; usage of the park (SOPARC); signs of care; auditory environment recordings; and visual greenness using 360 imagery. Health outcomes include device-based physical activity behavior (primary outcome); salivary cortisol (secondary outcome); and several downstream health outcomes. Exposure to the INT will be assessed through visual contact time and time spent in the park using GPS data. Changes in health outcomes between years and INT versus CNT will be tested using generalized linear (mixed) models. DISCUSSION: Our study will examine whether restored urban greenspaces increase physical activity and lower stress, with public health planning implications, where small changes in neighborhood greenspaces may have large health benefits in low-income neighborhoods. STUDY REGISTRATION: Registration: OSF Preregistration registered March 31, 2020. Accessible from https://osf.io/surx7.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".