Availability of activity-related resources in senior apartments: does it differ by neighbourhood socio-economic status?
Bibliographic record
Abstract
ABSTRACT Research has shown that the level of activity of the residents of a city's neighbourhood is related to the availability of activity-related resources. This study aimed to characterise the housing environment in which many older adults live by exploring what activity-related resources were available in senior apartment buildings in one Canadian city, Winnipeg. Of 195 senior apartment buildings in the city, 190 were surveyed to examine whether variation in the buildings' activity resources was related to neighbourhood characteristics, particularly socio-economic status. Resources were classified as those for physical activities (e.g. exercise classes), social activities (e.g. card games), and services (e.g. a grocery-store shuttle). The neighbourhood characteristics were taken from census data and included socio-economic and socio-demographic measures. The apartment buildings varied considerably in the resources available, and a positive relationship was found between neighbourhood income and physical and social activity programmes and services. Lower residential stability and a higher percentage of residents living alone were also related to the buildings' resource-richness, and senior apartment buildings with limited activity-related resources clustered in disadvantaged neighbourhoods. How senior apartments are resourced should be examined in relation to the neighbourhood in which they are located.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".