Built Environmental Aspects of a Disadvantaged Neighbourhood and Its Residents’ Quality of Life: A Scoping Literature Review
Bibliographic record
Abstract
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 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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".