Exploring the relationship between bird diversity and anxiety and mood disorder hospitalisation rates
Bibliographic record
Abstract
Abstract Natural environments provide a myriad of health benefits, yet the role of species diversity within these spaces remains underexplored. Bird diversity may yield mental health benefits for humans, through birdsong or feelings of connection to nature. In an initial effort to establish whether bird diversity may be linked with human health in a US context and to test the consistency in such trends from year to year, we combine widely available community (aka citizen) science data (eBird) estimating bird diversity across the state of Michigan with anxiety/mood disorder hospitalisation records (2008–18). We found a negative, significant association between bird species diversity and anxiety/mood disorder hospitalisations (β = −0.36, 95% CI = −0.69 to −0.04). The relationship between bird diversity and hospitalisations found at this scale is significant, given the potential for biodiversity to affect severe mental health outcomes. Thus, these initial findings should be further explored in studies with finer resolution of exposure to bird species and longitudinal or experimental designs that account for other demographic characteristics, risk factors and other neighbourhood features. If future studies confirm these findings, there are important implications for urban greening efforts, some of which are explicitly focused on increasing bird habitat.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".