How Does Travel Environment Affect Mood? A Study Using Geographic Ecological Momentary Assessment in the UK (Short Paper)
Bibliographic record
Abstract
Daily travel is a large part of life, and it is widely believed that our mood can be affected by the environment in which travel takes place. In this study, we investigate how environmental factors affect mood while performing daily travel activities using an app-based geographic ecological momentary assessment study. Our study (the WorkAndHome study) involved over 1000 participants tracked using a bespoke GPS mobile phone app in three cities (Birmingham, Leeds, and Brighton and Hove, UK) At the end of trips (i.e., when a stop in the GPS data was detected) we pushed a survey to participants asking them to score their current happiness and stress levels on a 7-point Likert scale. We combined individual GPS data with environmental data on green and blue spaces and weather conditions. We found that green and blue space availability and weather variables, such as daytime, apparent temperature, and visibility, significantly affect our happiness levels at the end of trips. While these weather factors were also significant predictors of stress level, availability of green and blue space was not. The results of this study provide fine-scale evidence from direct surveys about the associations between environment and weather and our moods when performing daily travel activities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".