Housing and psychosocial well-being during the COVID-19 pandemic
Bibliographic record
Abstract
The loss of psychosocial well-being is an overlooked but monumental consequence of the COVID-19 pandemic. These effects result not only from the pandemic itself but, in a secondary way, from the Non-Pharmaceutical Interventions (NPIs) made to curb the spread of disease. The unprecedented physical distancing and stay-at-home requirements and recommendations provide a unique window for housing researchers to better understand the mechanisms by which housing affects psychosocial well-being. This study draws on a survey conducted with over 2,000 residents of the neighbouring Canadian provinces of British Columbia and Alberta in 2021. We propose a new multi-dimensional model to examine the relationships between the Material, Economic, Affordances, Neighbourhood, and Stability (MEANS) aspects of housing and psychosocial well-being. Our analysis reveals the direct and indirect pathways by which deficiencies in each of these areas had negative effects on psychosocial well-being. Residential stability, housing affordances, and neighbourhood accessibility exert stronger direct impacts on psychosocial well-being than material and economic housing indicators (e.g. size of living space and tenure). Notably, we find no significant well-being differences between different homeowners and renters when we account for other housing MEANS. These findings have important implications for housing policy across pandemic and post-pandemic contexts, suggesting a need for research and policy focus on understanding housing and well-being in terms of non-material aspects, such as residential stability and affordances that housing provides.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".