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Record W2409657256 · doi:10.14430/arctic4564

Physical Activity Policy for Older Adults in the Northwest Territories, Canada: Gaps and Opportunities for Gains

2016· article· en· W2409657256 on OpenAlexaffvenueabout
Lauren A. Brooks-Cleator, Audrey R. Giles

Bibliographic record

VenueARCTIC · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRecreationPopulationGovernment (linguistics)Population ageingGerontologyGeographyEconomic growthBusinessPolitical scienceSocioeconomicsMedicineEnvironmental healthSociologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.345
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes3
Has abstractyes

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