Using Geospatial Analysis to Determine the Proximity of Community Gyms for a Population-Based Cohort of Young People with Cerebral Palsy
Bibliographic record
Abstract
Purpose: To quantify a perceived barrier to physical activity participation (proximity to exercise facilities) by investigating the distance a population-based cohort of young people with cerebral palsy aged 13-30 years lived from community gyms. Method: We developed a web-based application using open-access software that could be used to complete a geospatial analysis by simultaneously visualizing, describing, and estimating the location of young people with cerebral palsy, using deidentified data from a cerebral palsy register, in terms of distance and travel time to all available community gyms in one metropolitan city. The suitability of facilities for individuals was not investigated. Distance to the closest gym for participants was measured using the "as the crow flies" and "street network" methods. The proportion of the cohort living 5, 7, and 9 km from a community gym was calculated using the "as the crow flies" method. Distances and travel times to the closest gym for each person were calculated using the "street network" method. Data analysis used one-dimensional (median, quartiles) and two-dimensional (spatial median, bagplot) dispersion measures. Results: quartiles 4.4, 8.8) by car. For the two-dimensional analysis, the spatial medians were 3.7 km and 6.5 minutes. Conclusions: The open-access, web-based application that was developed can be used by physiotherapists and others to study proximity of clinical and community infrastructure for other populations in other cities and regions. In this study, most young people with cerebral palsy living in one metropolitan city had reasonable access by car to a community gym.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".