Social participation in older women and men: differences in community activities and barriers according to region and population size in Canada
Bibliographic record
Abstract
BACKGROUND: Social participation is a modifiable health determinant influenced by physical and social aspects of the environment. Little is known about aging women's and men's community activities and barriers according to region and population size. This study compared social participation, desire to participate more, and perceived barriers of aging women and men by Canadian region and population size. METHODS: A secondary analysis of the 2008-2009 cross-sectional Canadian Community Health Survey - Healthy Aging was done with 16,274 respondents aged 65+. Respondents were grouped into five regions [Atlantic, Quebec, Ontario, Prairies and British Columbia] and five population size groups [rural (< 1000 inhabitants); small urban (1000-29,999); medium urban (30,000-99,999); large urban (100,000-499,999); and metropolitan (≥500,000) areas]. Social participation was estimated by monthly frequencies of engagement in community activities. If they desired to participate more, respondents were asked to identify barriers to their participation from a list of 13 reasons. RESULTS: There were no differences in total social participation between regions but Prairies and Quebec respondents had the highest and lowest frequency, respectively, of activities with family and friends (5.4 and 4.3 activities/month; p = 0.01). Medium urban centers had the highest participation and metropolises, the lowest (17.4 vs 14.3 activities/month; p < 0.01). About one fourth of all respondents wanted to participate more, regardless of region or population size. Overall, women wanted to participate more than men (26.6 vs 20.7%; p < 0.001), especially in Ontario (28.3 vs 21.1%; p < 0.001) and British Columbia (30.1 vs 22.9%; p < 0.001). Men in Quebec were less likely than men in other regions to report "personal responsibilities" as a barrier to participation (p < 0.001). Men were more likely than women to report being "too busy", especially in rural areas (27.1 vs 6.5%; p < 0.001). Rural women were more likely than rural men to be constrained by transportation problems (15.1 vs 1.2%, p < 0.001). Unavailability of activities was more of a constraint in rural areas than metropolises (13.6 vs 6.0%, p < 0.001). CONCLUSIONS: Overall, there were no practical differences between women's and men's social participation. However, unavailability of activities and transportation problems suggest that local initiatives and further research on environmental characteristics are required to foster aging Canadians' participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".