Using crowdsourced data to assess the relationship between neighbourhood-level deprivation and the availability of inclusive leisure programmes in Canadian cities
Bibliographic record
Abstract
Monitoring neighbourhood-level access to resources can inform improved urban health. Big data approaches have shown some promise in capturing access to spatial resources such as green spaces, housing, amongst others. However, it is often difficult to capture resources that are not spatially observable such as programmes. For this project, we linked data from a digital listing of inclusive leisure programmes to data on neighbourhood-level deprivation, to explore the relationship between both factors, and how to strengthen approaches for capturing access to health-promoting programmes. Using cross-sectional secondary data analysis, we linked information on material and social deprivation levels in three major census metropolitan areas of Canada to information on the availability of adaptive leisure programmes as listed on the Jooay App (www.jooay.com). Higher availability of inclusive leisure programmes was directly linked to higher social deprivation and inversely linked to higher material deprivation. Inclusive leisure programmes were more available for populations with physical and intellectual impairments and autism spectrum disorders, than sensory and behavioural challenges. Our study suggests potentially differing relationships between forms of deprivation and the availability of inclusive programs and a need for stronger consideration of disability diversity. We also note considerations for using big data to inform urban health.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".