Social Housing and Health in Manitoba
Bibliographic record
Abstract
ABSTRACT ObjectiveFourteen years of social housing data (1995-2008) were acquired from the provincial government. This allowed for an unprecedented opportunity to describe the population of individuals living in social housing and, through data linkage, to compare them to the rest of the province on a number of health and social indicators. ApproachUsing data from the entire population of the province of Manitoba, Canada, cross-sectional comparison were made between those living in social housing and those not on 19 indicators of morbidity, mortality, health care utilization and social development. Regression models were developed to control for age, sex, region of residence, comorbidities, income and neighborhood level SES. Results50% of the population in social housing are under the age 20, 75% are female and 50% of applicants receive some form of income assistance. As expected there are significant differences on most health status measures when compared to individuals not in social housing. However, after controlling for confounding factors most differences between the two groups disappear indicating that there is no independent effect of living in social housing. A few exceptions were noted on measures of total respiratory morbidity, mammography and high school completion rates, the later showing a very significant interaction with neighborhood level SES. ConclusionDespite overall poor health status, after controlling for income and other confounding factors individuals in social housing score no worse on many measures of health care utilization and prevention. High school completion rates, in particular, showed a very strong interaction with neighborhood level SES. Policy implications of this research are discussed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".