Exploring the relationships between soundscape quality and public health using a systems thinking approach
Bibliographic record
Abstract
Urban soundscapes significantly influence public health, with sound quality affecting well-being and social value. While traditional noise control has emphasized harm reduction, soundscape studies propose that managing sound environments can promote health benefits. This study explores the complex relationships between soundscape quality and public health using a systems thinking approach. In a participatory workshop with 21 experts from fields such as urban planning, environmental psychology, and acoustics, a causal loop diagram (CLD) was developed to illustrate the interactions between soundscape quality and public health variables. The CLD revealed key feedback loops and intervention points, organized around themes of socio-economic impact, environmental justice, biodiversity, and soundscape design. Findings highlight that while soundscape quality can enhance community well-being, increased economic value may drive gentrification, altering the social structure and reducing sound source diversity. Additionally, the role of soundscape quality in biodiversity suggests both co-benefits and ecological risks. This study demonstrates the potential of systems thinking to guide interdisciplinary approaches in soundscape management, identifying strategic pathways to inform future research and policy development for equitable and health-promoting urban environments.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.000 | 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".