Falling forward into new selves and spaces: Transitions from homeless to housed for individuals with mental illness
Bibliographic record
Abstract
This thesis focuses on how individuals who have been homeless, with a history of mental health problems, transition to life in stable housing. I approached the study of this transition through the lens of ontological security to focus on two interrelated dimensions of human wellness: the need for secure spaces and the need for belonging. Ontological security requires a sense of continuity and confidence in both spatial and relational networks. In Study 1, I used regression analyses to describe the relationships between predictor variables (i.e., demographic, health, and traumatic life experiences) and the outcomes of housing stability and community functioning. The two outcome variables had distinct predictor variables suggesting that characteristics associated with high levels of housing stability are not necessarily associated with high levels of community functioning and vice versa. Further, participants enrolled in a Housing First (HF) intervention were more likely to report high housing stability and community functioning relative to individuals accessing traditional community services. The findings also suggest that social determinants of health inequality, including Indigenous ethnicity, are associated with chronic homelessness and that certain early life experiences (i.e., foster care, low levels of education) are associated with poorer outcomes. In Study 2, narrative and photo-elicitation methods were used to study the ways that people with a history of homelessness and mental health challenges reconstruct a sense of self after moving into stable housing. Participants actively reconstructed their identities by integrating their homeless, past, “nobody” selves into their housed “somebody” identities. The temporal structure of their stories suggests that while street living was akin to being stuck in a repeat cycle, housed living allowed for new openings and movement to take steps and “fall forwards”. One of the ways that participants integrated past and present selves in their narratives was through their connection to their sensed world – that objects, scents, and sounds allowed participants to ground themselves in unfamiliar apartment spaces. Participants expressed the ever present fear of falling backwards to their past lives and the remaining discrimination from others that became an obstacle to re-selfing and feeling at home where they lived.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".