Mental Health Outcomes in Barcelona: The Interplay between Gentrification and Greenspace
Bibliographic record
Abstract
Greenspace is widely related to mental health benefits, but this relationship may vary by social group. Gentrification, as linked to processes of unequal urban development and conflict, potentially impacts health outcomes. This study explores the relationships between greenspace and mental health and between gentrification and mental health associations. It also further examines gentrification as an effect modifier in the greenspace-mental health association and SES as an effect modifier in the gentrification-mental health association. We used cross-sectional Barcelona (Spain) data from 2006, which included perceived mental health status and self-reported depression/anxiety from the Barcelona Health Survey. Greenspace exposure was measured as residential access to (1) all greenspace, (2) greenways and (3) parks in 2006. Census-tract level gentrification was measured using an index including changes in sociodemographic indicators between 1991 and 2006. Logistic regression models revealed that only greenways were associated with better mental health outcomes, with no significant relationship between mental health and parks or all greenspace. Living in gentrifying neighborhoods was protective for depression/anxiety compared to living in non-gentrifying neighborhoods. However, only residents of gentrifiable census tracts benefited from the exposure to greenways. SES was not found to be an effect modifier in the association between gentrification and mental health. Future research should tackle this study's limitations by incorporating a direct measure of displacement in the gentrification status indicator, accounting for qualitative aspects of greenspace and user's perceptions. Gentrification may undermine the health benefits provided by greenspace interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".