Physical Activity Policy for Older Adults in the Northwest Territories, Canada: Gaps and Opportunities for Gains
Bibliographic record
Abstract
In the Northwest Territories (NWT), Canada, the population of older adults is increasing, and this population reports much poorer health than other age cohorts. Given the number of benefits that physical activity (PA) can have for older adults, we analyzed policies concerning older adults and PA of both the NWT government and non-governmental organizations in the health, recreation, and sports sectors. Our findings indicate that although the majority of the organizations had no PA policies specific to older adults or Aboriginal older adults, some organizations completed all five stages of the policy cycle (agenda setting, policy formulation, decision making, implementation, and evaluation). Our analysis suggests that PA for older adults is not on the agenda for many organizations in the NWT and that often the policy process does not continue past the decision-making stage. To address the need for connections between all stages of the policy cycle, we suggest that organizations collaborate across multiple sectors and with older adults to develop a territory-wide, age-friendly rural and remote community strategy that is applicable to the NWT. Prioritizing age-friendly communities would, in turn, facilitate appropriate PA opportunities for older adults in the NWT and thus contribute to a healthier aging population.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".