Housing and mental health inequalities during COVID-19: the role of income and housing support measures
Bibliographic record
Abstract
The COVID-19 pandemic negatively impacted people’s mental health and wellbeing. Using a national dataset of >11,000 Australians collected before and during the first two years of the pandemic, this study examines housing and mental health effects of COVID-19, and the extent to which access to government income support (social security measures, crisis payments and wage subsidy), early superannuation withdrawal, mortgage and rent relief, and tenant eviction moratoriums offered protection. Results show that the mental health gap between private rental and more secure housing tenures and between good- and poor-quality housing widened during the pandemic. Government income support provided a social safety net and was important in buffering housing instability especially when strong eviction moratoriums were lacking. Mortgage relief measures were associated significantly lower risks of housing affordability stress. Strong eviction moratoriums were effective in reducing risks of residential instability and forced moves. The pandemic exposed health vulnerabilities generated from people’s housing circumstances, reinforcing the need for public policies to address these social inequities to improve health and wellbeing. Findings emphasise the importance of tenure security, housing quality and enforcement of rental market interventions during disasters and identify the benefits of policies providing income support and strong eviction protection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".