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Record W1592951543 · doi:10.7939/r3c37z

Exploring barriers and facilitators to the implementation of healthy aging policy in suburban planning and transportation departments

2011· article· en· W1592951543 on OpenAlexaboutno aff
Deborah Rae Rawson

Bibliographic record

VenueUniversity of Alberta Library · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Plan (archaeology)Public policyQualitative researchTransportation planningBusinessAffect (linguistics)Built environmentPublic transportEnvironmental planningPolitical sciencePublic relationsTransport engineeringGeographySociologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Purpose: The purpose of this research was examine potential barriers and facilitators to the implementation of healthy public policy related to aging, specifically in municipal planning and transportation departments, in the suburban context. Research Questions: What are the factors which influence successful implementation of healthy public policy in municipal and, specifically, suburban contexts? How do suburban planning and transportation policies interface with policies aimed at physical environmental changes to shape the built environment in ways that are theorized to affect seniors’ health? Methods and Results: A qualitative case study approach was used to examine Strathcona County’s Older Adults Plan (2009a). Strathcona County is a municipality bordering Edmonton, Alberta, Canada. Data was collected through document reviews and 19 semi-structured interviews with 21 individuals involved in the development of the plan or potentially affected by its implementation. Results were triangulated in a final analysis of policy implementation capacity in Strathcona County.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.592

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.290
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2011
Admission routes1
Has abstractyes

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