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Built Environmental Aspects of a Disadvantaged Neighbourhood and Its Residents’ Quality of Life: A Scoping Literature Review

2024· article· en· W7125834226 on OpenAlexaff
Paniz Mousavi Samimi, Gina Dimitropoulos, Brian Robert Sinclair

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDisadvantagedNeighbourhood (mathematics)Scope (computer science)ScopusThematic analysisBuilt environmentQuality (philosophy)Quality of life (healthcare)

Abstract

fetched live from OpenAlex

The challenges confronting individuals in disadvantaged neighbourhoods are often exacerbated by the adverse effects of a substandard built environment on their Quality of Life (QoL), a subject extensively examined in existing literature. However, delving deeper into the scope of these investigations is imperative to comprehend the thoroughness with which these impacts have been explored. Thus, the present study seeks to identify current research trends and gaps by synthesising published papers on the built environment of disadvantaged neighbourhoods and its association with the QoL of residents. Adhering to the PRISMA guideline, this scoping review systematically gathered knowledge on the designated topic through searches in Web of Science and Scopus databases. Data from 73 eligible studies were meticulously extracted and subjected to thematic analysis for coding. The results have elucidated a comprehensive classification of the various aspects of the built environment in disadvantaged neighbourhoods, indicators and metrics associated with QoL, and the intricate interplay between the built environment’s facets and QoL indicators. By pinpointing the prevailing research trends and identifying gaps in the current literature, the outcomes provide valuable insights that can guide future research endeavours in this domain.

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)0.001
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: none
Teacher disagreement score0.574
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.378
Teacher spread0.340 · 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
Published2024
Admission routes1
Has abstractyes

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