Street trees and mental health: developing systems thinking-informed hypotheses using causal loop diagraming
Bibliographic record
Abstract
We considered the relationship between street trees and mental health with the aim of developing systems thinking-informed hypotheses to improve the implementation and evaluation of this popular nature-based solution (NBS). We integrated qualitative and quantitative evidence using causal loop diagraming (CLD), and then further analyzed and extended these diagrams with the aid of systems archetypes to identify key system structures. From these CLDs, we identified three systems thinking-informed hypotheses: 1) although there are many ways in which street trees may improve mental health, tree health is critical in realizing many of these benefits and minimizing dis-benefits; 2) communities which have benefited from street trees in the past are more likely to be able to advocate for additional trees, further entrenching historical inequities in street tree distribution; and 3) efforts to address these inequities through new tree planting initiatives may ultimately fail or even exacerbate existing challenges if they do not include sustained resources for tree maintenance, with direct and indirect impacts on inequities in mental health. Using a systems thinking lens was a useful way to deeply consider a purported but under-theorized co-benefit of a popular nature-based solution and identify policy-relevant hypotheses to guide future research.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".