Correspondence of perceived vs. objective proximity to parks and their relationship to park-based physical activity
Bibliographic record
Abstract
BACKGROUND: Parks are key environmental resources for encouraging population-level physical activity (PA). In measuring availability of parks, studies have employed both self-reported and objective indicators of proximity, with little correspondence observed between these two types of measures. However, little research has examined how the degree of correspondence between self-reported and objectively-measured distance to parks is influenced by individual, neighborhood, and park-related variables, or which type of measure is more strongly related to physical activity outcomes. METHODS: We used data from 574 respondents who reported the distance to their closest park and compared this with objective measurements of proximity to the closest park. Both indicators were dichotomized as having or not having a park within 750 m. Audits of all park features within this distance were also conducted and other personal characteristics and neighborhood context variables (safety, connectedness, aesthetics) were gleaned from participants' survey responses. Participants also completed detailed seven-day PA log booklets from which measures of neighborhood-based and park-based PA were derived. RESULTS: Agreement was poor in that only 18% of respondents achieved a match between perceived and objective proximity to the closest park (kappa = 0.01). Agreement was higher among certain subgroups, especially those who reported engaging in at least some park-based PA. As well, respondents with a greater number of parks nearby, whose closest park had more features, and whose closest park contained a playground or wooded area were more likely to achieve a match. Having a ball diamond or soccer field in the closest park was negatively related to achieving a match between perceived and objective proximity. Finally, engaging in at least some park-based PA was not related to either perceived or objective proximity to a park, but was more likely when a match between and perceived and objective proximity occurred. CONCLUSION: Poor levels of correspondence were observed between self-reported and objective proximity to parks, but certain individual, neighborhood, and park variables increased the likelihood of a participant being aware of local parks. Future research should examine how people conceptualize parks and what urban and park planners can do to increase awareness and use of these community assets.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".