Healthy Outside-Healthy Inside: The Human Health & Well-being Benefits of Alberta's Protected Areas - towards a benefits-based management agenda
Bibliographic record
Abstract
This report details the results of an empirical study that examined perceived health and well-being motives and benefits among visitors to a sample of Alberta’s parks and protected areas. The study revealed several major findings with important policy and management implications. First, the human health and well-being benefits that the visitors expected to receive from visits were perceived to be a major personal motivation in the choice to visit Alberta protected areas. The most important motivation factors identified by respondents were psychological and emotional well-being (89.1% of visitors ranked this important), social well-being (88.3%), physical well-being (80.3%), and environmental well-being (79.4%). Second, the perceived benefits that visitors received from their protected areas experiences were substantial. The most frequently reported improvements were related to psychological and emotional (90.5%), social (85%), and physical well-being (77.6%). Interestingly, women perceived greater benefits than men associated with their visit, especially with respect to spiritual, social, and psychological and emotional well-being. Research findings substantiate the need for park agencies to better understand the motivations of visitors representing different social and population subgroups (e.g., youth, elderly, couples, etc.) in order to inform and develop policies and visitor experience programs in support of health and well-being related pursuits. Important policy and management implications for both park managers and health care professionals are highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".