The effect of dwelling size on the mental health and quality of life of female caregivers living in informal tiny homes in Hong Kong
Bibliographic record
Abstract
BACKGROUND: Although the adverse impact of substandard informal housing has been widely documented, most studies concentrated on developing societies, thereby leaving informal housing in developed regions underexplored. This study examines Hong Kong, where limited dwelling size is a distinctive feature that characterises informal housing, to explore the impact of housing informality on mental health, with a particular focus on dwelling size. It centers on subdivided units (SDUs), which are tiny compartments partitioned from a large domestic quarter, to understand how housing informality and housing size affect the mental well-being of female caregivers, who typically bear the brunt of the housework. METHODS: In partnership with nongovernment organisations in three SDU-abundant districts, this mixed-methods study conducted a survey on 413 female caregivers aged 18-65 and qualitative research combining ethnographic observations and in-depth interviews on 36 families living in SDUs in Hong Kong between 2021 and 2023. The mental health outcomes and health-related quality of life (HRQoL) of the participants were assessed by using the Depression, Anxiety, and Stress Scale-21 and a EuroQol-5 Dimension-5 Level questionnaire. RESULTS: was associated with increased likelihood of experiencing anxiety and depression and reduced HRQoL. Cramped living space adversely affected the caregivers' well-being through the 1) physical, 2) relational and 3) personal aspects of home experiences. Negative experiences at home can cause housework burnout, exacerbate family conflicts and lead to feelings of repression and low self-efficacy. CONCLUSIONS: This study contributes to the understanding of the consequences of housing informality in diverse geographical contexts and illuminates the effect of dwelling size by identifying the mechanisms through which housing size can affect the mental well-being of residents, which may vary depending on their family status. The findings yield important policy implications, including the need to establish a minimum space standard for subdivided residential dwellings and ensure equitable access to community spaces for deprived families.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".