Sleep and Quality of Life in Urban Poverty: The Effect of a Slum Housing Upgrading Program
Bibliographic record
Abstract
STUDY OBJECTIVES: To evaluate the effect of a housing transition on sleep quality and quality of life in slum dwellers, participating in a slum housing upgrading program. DESIGN: Observational before-and-after study with a convergent-parallel mixed method design. SETTING: Five slums located in the metropolitan area of Buenos Aires, Argentina. PARTICIPANTS: A total of 150 slum dwellers benefited by a housing program of the nonprofit organization TECHO (spanish word for "roof"). INTERVENTIONS: Participants moved from their very low-quality house to a basic prefabricated 18 m(2) modular house provided by TECHO. MEASUREMENTS AND RESULTS: The Pittsburgh Sleep Quality Index (PSQI) and World Health Organization Quality of Life brief scale (WHOQOL-BREF) were administered before and after housing upgrading. Data about housing conditions, income, education, sleeping conditions, and cardiovascular risk were also collected. Semistructured interviews were used to expand and nuance quantitative data obtained from a poorly educated sample. Results showed that sleep quality significantly increased after the housing program (z = -6.57, P < 0.001). Overall quality of life (z = -6.85, P < 0.001), physical health domain (z = -4.35, P < 0.001), psychological well-being domain (z = -3.72, P < 0.001) and environmental domain (z = -7.10, P < 0.001) of WHOQOL-BREF were also improved. Interviews demonstrated the importance of serenity for improving quality of life. CONCLUSIONS: A minimal improvement in the quality of basic housing can significantly increase sleep quality and quality of life among slum dwellers. Understanding sleep and daily life conditions in informal urban settlements could help to define what kind of low-cost intervention may improve sleep quality, quality of life, and reduce existent sleep disparity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".