Predictors of Return Visits to Trails with Self-Guided Materials for Children
Bibliographic record
Abstract
Participation in outdoor recreation can positively contribute to physical and emotional well-being. However, questions remain regarding the most effective way to implement programs that promote childhood engagement in outdoor recreation. Using seven years of data, we explored factors driving visitation to trailheads that offer self-guided materials for children at parks and recreation facilities of the Kids in Parks program. We evaluated the demographic, managerial, and physical predictors of visitation to the 115 trails included in the program. Of 769 visitors who made at least one return visit to a TRACK Trail, 305 (39.7%) returned to the same trail, 675 (87.8%) returned to a different trail, and 211 (27.4%) did both. Using multiple linear regression, we found that repeat visits to any trail and new trails increased (p<0.01) when the trail was in a state park or a national forest. Return visits to new trails were more likely to take place at locations without a visitor center, and at locations that were located farther away from visitors’ homes. Visitors who made any return trail visits came from areas with significantly higher unemployment rates, compared to visitors who did not make repeat visits. The results of this study have broad applications in creating inclusive recreation opportunities for all residents, and guiding communities as they make management decisions. Subscribe to JPRA
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".