Breaking down barriers in the Village of New Minas: understanding what makes an age-friendly community
Bibliographic record
Abstract
The province of Nova Scotia has both the highest proportion of older adults and persons with disabilities in Canada. Nova Scotia has a responsibility to make sure the needs of all people, but especially those who are older and living with a disability, are met. The growing number of older adults and persons with disabilities is not only occurring in Nova Scotia. This trend is also recognized by the World Health Organization, who say that the world's population is aging rapidly. Therefore, age-friendly communities, communities where people regardless of age and ability feel welcome and included, are necessary to enhance accessibility and inclusion for all. This research explored the perspectives of older adults who reside in the Village of New Minas, a rural community in Nova Scotia, to understand how New Minas can implement age-friendly practices. The results emphasized a need to improve accessibility in the built environment, such as sidewalks, the community complex centre, and the public transportation system. In addition, the participants had valuable insights on how to improve communication and community engagement within New Minas. New Minas, similar to other areas of Nova Scotia, is responding to an increase in migration to Nova Scotia and is currently working on plans to expand. Therefore, understanding the needs of older adults to create an age-friendly community will be essential as New Minas starts to expand their community.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".