Measuring Indigenous homelessness: Findings from Our Health Counts Toronto
Bibliographic record
Abstract
OBJECTIVES: Our Health Counts (OHC) Toronto, an Indigenous population database which addresses gaps in urban health information, was used to measure Thistle's (2017) 12 dimensions of Indigenous homelessness. Using this framework, we examine the sociodemographic characteristics of First Nations, Inuit, and Metis (FNIM) adults living in Toronto, the 12 dimensions as experienced by this population, and the distinctions between FNIM adults who were and those who were not experiencing physical homelessness. METHODS: Respondent-driven sampling (RDS)-II proportions and 95% confidence intervals were produced from the database (n = 915 FNIM adults) to describe key sociodemographic characteristics of the population and to estimate the proportion and number of dimensions of Indigenous homelessness experienced by FNIM adults. Results were compared between those who were and those who were not living physically homeless. RESULTS: This study shows that 27.3% of FNIM adults in Toronto were living physically homeless. The proportion of homelessness was significantly higher among males, adults aged 26 to 54, and unemployed individuals. Using the OHC database, 7 of the 12 dimensions were measurable. Almost all FNIM adults had experienced one or more of the 7 measurable dimensions. The most common were cultural disintegration and loss, mental disruption and balance, contemporary geographic separation, and relocation and mobility. These dimensions were significantly more common among FNIM adults experiencing physical homelessness. CONCLUSION: Results show that FNIM adults living physically homeless are more likely to experience other dimensions of homelessness. Using existing data, 5 of the 12 dimensions were not measurable. This points to a critical need to develop new survey tools to fully understand the historical, environmental, social, political, spiritual, and emotional factors that influence pathways into homelessness among FNIM populations.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| 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".