Surveillance of Social and Geographic Inequalities in Housing-Related Issues: The Case of the Eastern Townships, Quebec (Canada)
Bibliographic record
Abstract
Even though health inequalities are conditioned by many aspects of the environment, much of the existing research focuses on the social environment. This emphasis has the effect to neglect other environmental aspects such as its physical dimension. The physical environment, which is linked to housing conditions, may contribute to the uneven distribution of health. In this study, we examined 19 housing-related issues among a representative sample of 2,000 adults residing in a Quebec (Canada) health region characterized by a mix of rural, semi-rural, and urban areas. The distribution of these issues was examined according to socioeconomic and geographic indicators of social position. Summary measures of inequalities were assessed. Our results showed that the prevalence of nearly all housing-related issues was higher among low-income households compared to more affluent ones. Highly educated individuals showed better housing conditions, whereas different issues tended to cluster in deprived or densely populated areas. To conclude, we observed steep gradients between social class and poor housing conditions. This may explain a substantial part of health inequality on the regional scale. The surveillance of housing-related issues is therefore essential to properly inform and mobilize local stakeholders and to develop interventions that target vulnerable groups on this level.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".