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Record W2954577748

The Effects of Healthy Built Environments on Perceived Quality of Life

2018· article· en· W2954577748 on OpenAlexaffabout
Melissa Van Yken

Bibliographic record

VenueStudent Research Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMacEwan University
Fundersnot available
KeywordsWalkabilityNeighbourhood (mathematics)Built environmentPerceptionQuality of life (healthcare)GerontologyEnvironmental healthPsychologySurvey data collectionApplied psychologyGeographyMedicineEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

The environment built around us by political and municipal governing bodies plays an important role in the general populations’ accessibility to resources needed for life satisfaction. By having a greater access to health knowledge, important amenities, and engagement in physical activity, our overall health and well-being can be improved. The goal of this study is to examine the walkability and accessibility within neighbourhoods and their effects on perceived quality of life in participants residing in Edmonton, Alberta. This research will further build on previous data obtained demonstrating direct linkage between neighbourhood walkability and increased health benefits. In addition, by improving overall health, communities can further increase the life satisfaction of its inhabitants through socio-spatial analysis. A rating calculated by the Walk Score algorithm will be used to identify the walkability of Edmonton neighbourhoods. This information will then be compared to secondary data obtained through a survey completed by the City of Edmonton (Edmontonians’ Perception Survey – Quality of Life Survey) to determine if the increased walkability within a neighbourhood has an effect on the participants’ perceived quality of life. Discipline: Mathematics Faculty Mentor: Dr. Karen Buro

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.142
GPT teacher head0.498
Teacher spread0.356 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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