Age-friendly community capacity building in Newfoundland and Labrador
Bibliographic record
Abstract
Although the number of communities in Canada implementing Age-Friendly programming is growing every year, few of these programs have been evaluated. The current research used social capital theory to study and to evaluate age-friendliness in Newfoundland and Labrador. Social capital is a useful theoretical framework when studying the impact of Age-Friendly community capacity building. Some communities may experience few challenges when bringing together volunteers and community members, whereas for others, this may be a major obstacle. A mixed methods approach to data collection included a) surveys of 23 communities, including surveys completed by 45 individual Age-Friendly committee members, b) an analysis of existing census and health data, and c) qualitative focus groups or interviews with 35 committee members in 11 communities, and with 43 seniors in 4 communities. In total, 108 people and 24 communities participated in this research. Communities were primarily small in population and were located in rural areas of Newfoundland and Labrador. Nearly all provincial geographic regions were represented in the analysis. Participants from communities with a high overall satisfaction with life had a significantly higher social capital score, and participants from communities with a high income per capita had a significantly lower sense of community than those living in a medium income per capita community. Population change significantly predicated sense of community, such that communities experiencing population outmigration experienced a lower sense of community. Qualitative findings indicated benefits for communities related to intergenerational programming, and for seniors, related to health, social support, and technological education. Outmigration both increased the need for Age-Friendly programming given aging populations, and created a challenge for program development given volunteer burnout, typically addressed by community capacity building and maximizing social capital resources. Those communities who experienced lower levels of bonding social capital typically had more problems developing this capacity. Overall, community social capital was a helpful framework in understanding the success of community-based initiatives in rural or small-town Newfoundland and Labrador.
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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 | 0.002 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".