Do parks help us feel well? An investigation of park quality, and the wellbeing impacts from park experiences in Canada
Bibliographic record
Abstract
Parks and protected areas in Canada may contribute to improved population health by providing venues for social interaction, settings for physical activity, and spaces for mental restoration. There is a plethora of evidence linking parks to physical and mental health, however, there is a dearth of evidence about how parks may affect wellbeing. This dissertation investigates how the wellbeing of Canadians is associated with geographic accessibility to, exposure within, and the quality of parks. This investigation is carried out using three different methodologies. Data collection occurred in Halifax, Nova Scotia; London, Ontario; Montreal, Quebec; North Vancouver, British Columbia; Prince Albert, Saskatchewan; Saskatoon, Saskatchewan; Winnipeg, Manitoba; and Yellowknife, Northwest Territories. First, the amenity, infrastructure, and safety quality of parks were assessed using a standardized audit tool adapted from prior evaluation tools. Quality scores were then compared with indices of multiple deprivation of the population within each study area to evaluate the extent of any socio-spatial inequalities. Notably, park quality does not significantly differ based on socioeconomic or demographic composition of the surrounding neighbourhood. Instead, park quality differs based on measures of residential stability and situational vulnerability. Second, a survey was undertaken on a large population of Canadians in the study areas to understand how geographic accessibility to parks may affect wellbeing while also considering demographic and attitudinal factors. Geographic accessibility to parks did not significantly correlate with better or worse wellbeing scores. Third, a novel geographic ecological momentary assessment research platform was developed and implemented to assess how actual geographic exposure to parks affects perceived wellbeing. A sample of survey participants were asked to complete a short survey via the platform multiple times per day, as well as whenever they dwelled inside or exited a park. The wellbeing scores of participants who were exposed to parks in the past 24 hours did not significantly differ from those who had no exposure to parks. The research platform described in the dissertation is a major contribution to health geography research methods. Overall, this dissertation provides foundational evidence to further explore public health and parks planning-based interventions to improve population health and wellbeing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".