Rooming Houses and Health: A Case Study
Bibliographic record
Abstract
Background: Rooming house residents have high rates of morbidity and mortality, yet little is known about why this disparity in health exists. \nResearch Question: How are rooming houses linked to health? \nCase: Social exclusion of rooming house residents in downtown Ottawa, bounded by the neighborhood, and Ottawa’s political policies at the time of data collection (September 2019-June 2020). \nMethodology: A single embedded descriptive case study was informed by multiple sources of evidence, and involved a community advisory group (CAG). Rooming house residents took photos, participated in a community walk-about with participant observations and attended a focus group. Two additional focus groups were conducted; one with fellow rooming house residents, another with the CAG. Interviews with rooming house front-line service providers and a secondary data set of homeless service measures also informed the case. \nFindings: 1. Rooming house residents (n=10) took 112 photos, and (n=8) took part in a focus group where two broad themes emerged: Housing is health care, and just managing today. 2. Interviews with front-line service providers (n=11) focused on two themes: There are many costs to living in a rooming house, and rooming house front-line service providers wear many hats. 3. Between a sample of sheltered homeless (n = 60) and rooming house residents (n=52), there was no difference found for several health indicators, including frequency of care received in the emergency room, hospitalization as an inpatient, and if substance use made it difficult to stay or afford housing. Focus groups with rooming house residents who did not take photos (n=10) and the GAG (n=6) contributed to persona co-creation revealing financial and contextual factors affecting the health of rooming house residents. \nConclusion: The shared spaces of rooming houses create a tension between offering community and creating a risk environment. The negative health consequences to living in a rooming house are mitigated by the many roles that rooming house front-line service providers play in filling gaps. This study suggests the need to definitively position rooming house residents on the housing continuum in order to ensure equitable distribution of resources to optimize the health of this vulnerable population. \n
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".