Exploring housing experiences in Canada: Insights from surveys and photovoice
Bibliographic record
Abstract
Housing crises around the world can be addressed, in part, by an increased reliance on higher density housing, making multi-unit residential buildings (MURBs) a key solution. This pilot study explores how housing type – MURBs versus single-family homes (SFHs) – shapes residents’ lived experiences, satisfaction with environmental comfort conditions, and community engagement. Using surveys and Photovoice, a participatory visual communication method, we assessed both social and environmental aspects of residential life. Results showed that SFH residents reported higher satisfaction across domains like space quality, natural light, comfort, and neighbourly interactions. Photovoice findings echoed these patterns, linking SFHs to feelings of spaciousness and family connection. On the other hand, MURB residents described issues such as lack of adequate space, high noise levels, limited daylight, restricted access to nature, and an unmet desire for community ties despite closer proximity to neighbours than SFH dwellers. Our findings suggest that housing satisfaction is shaped by a complex interplay of physical, social, and environmental factors. Hence, designing for density must go beyond unit counts to prioritize livability through improved environmental comfort, access to natural spaces and lighting, accessible communal spaces, and human-centered design strategies.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".