Environmental Preferences and Concerns of Recreational Trail Runners
Bibliographic record
Abstract
Trail running is a fast-growing sport, linked to improvements in both physical and psychological well-being. Despite its popularity, the preferences of trail runners are not well known. The objective of this study was to examine the environmental preferences and concerns of trail runners with respect to age and gender. We conducted a cross-sectional survey of recreational trail runners. A total of 548 people responded, of which 50.1% of respondents were women and 44.2% were men. The sample was distributed relatively evenly across age groups, up to 54 years; respondents over 55 represented only 9.4% of the sample. Comparisons of runner characteristics by gender indicated significant differences (p < 0.05) according to age, distance run per week, and number of days run per week. Certain runner preferences also differed significantly by gender, including importance of running around others, the type of trail races they seek, and whether or not they like to seek “vert” or elevation in their runs. Major concerns for both genders while running included lack of cell reception (Men: 33.8%; Women: 50.8%) and getting lost (Men: 26.8%; Women: 35.5%). Comparisons of the results of this study help to strengthen our understanding of trail runners’ environmental preferences and concerns and can be used to guide future design and maintenance of trail environments to encourage greater participation in the sport.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".