The ‘healthy parks–healthy people’ movement in Canada: progress, challenges, and an emerging knowledge and action agenda
Bibliographic record
Abstract
In this article, we outline progress and challenges in establishing effective health promotion tied to visitor experiences provided by protected and conserved areas in Canada.Despite an expanding global evidence base, case studies focused on aspects of health and well-being within Canada's protected and conserved areas remain limited.Data pertaining to motivations, barriers and experiences of visitors are often not collected by governing agencies and, if collected, are not made generally available or reported on.There is an obvious, large gap in research and action focused on the needs and rights of groups facing systemic barriers related to a variety of issues including, but not limited to, access, nature experiences, and needs with respect to health and well-being outcomes.Activation of programmes at the site level continue to grow, and Park Prescription programmes, as well as changes to the Accessible Canada Act, represent significant, positive examples of recent cross-sector policy integration.Evaluations of outcomes associated with HPHP programmes have not yet occurred but will be important to adapting interventions and informing cross-sector capacity building.We conclude by providing an overview of gaps in evidence and practice that, if addressed, can lead to more effective human health promotion vis-à-vis nature contact in protected and conserved areas in Canada.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".