Exploring the Effectiveness of Ecological Momentary Assessment and Video at Eliciting the Impact of Park Features on Human Health and Well-being in a Protected Area Context
Bibliographic record
Abstract
It is increasingly recognized that contact with nature in parks and other forms of protected areas provides benefits to visitors’ physical, mental, social, spiritual, and intellectual health and well-being. However, the methods used to assess these outcomes, including the influence of different environmental features that lead to these benefits, is under-researched. To address this gap, this study used a mixed methods design to explore the effectiveness of using Ecological Momentary Assessment (EMA) and video to assess the impact of various park features on the self-perceived (subjective) health and well-being of visitors. Participants were prompted by a mobile application on their smartphone to complete an in-situ survey on their device each time they entered a pre-defined geofence location within Arrowhead Provincial Park, a protected area in Ontario, Canada, over a three-day period in the winter season. The survey included the Brief Emotional Experience Scale (BEES) to measure participants' mental well-being and a video question to assess which park features were impacting participants' perceptions of health and well-being. Participants also provided feedback about their experiences during the study via a study exit survey. The results of this pilot study provided evidence that using a video question with EMA has the potential to be effective in understanding the relationships between park features and health and well-being. This study revealed a high in-situ survey response rate and reasonable temporal and spatial latency. The results also provided evidence that using video was very effective at eliciting park features and visitor feelings of health and well-being as participants reported hundreds of features and feelings within their videos. However, the video was much less effective at eliciting a direct relationship between features and health and well-being. Based on the results of this study, methodological recommendations for using EMA and video in a park context are provided. These findings can help researchers further the understanding of the relationship between park features and health and well-being to better inform visitor planning and management in a protected area context.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".