Exploring Contributors to Unsheltered Homelessness and Perceived Health Impacts Among Calgary’s Homeless Population
Bibliographic record
Abstract
Within the population of people experiencing homelessness (PEH), there exists a subgroup known as ‘rough sleepers’ who do not access emergency shelters and instead sleep outside on the streets or in encampments. The study's objectives are to explore (1) the contributors to an individual’s lack of shelter use and (2) the perceived health impacts of being unsheltered. Data was collected through individual semi-structured interviews (n=20) with unsheltered PEH in Calgary. We used convenience sampling through a partnership with street outreach organizations to approach individuals. Interviews were digitally recorded and transcribed verbatim. NVivo software was used to conduct a thematic analysis to identify, analyze, and report patterns within the data. Due to the rich patterns identified during preliminary coding, themes pertaining to each objective will be reported and interpreted independently. We identified five themes related to emergency shelter avoidance (objective 1): 1. Current shelter structures and policies limit individuals' capacity to respond to their needs. 2. There is no consensus in regard to substance use within emergency shelters. 3. Individuals require support from other people no matter where they are sleeping. 4. Individuals seek safety in and outside the emergency shelters. 5. There are inadequate resources in the community. We identified four themes related to perceived health impacts (objective 2): 1. Individual health is negatively impacted no matter where people sleep. 2. Individuals frequently choose between their livelihood and their health. 3. Trauma and past injury resulting in complex mental health concerns limit one’s ability to access shelters. 4. Despite the many challenges, individuals sleep outside because it makes them feel healthier. By studying the complex and multifaceted issues that impact an individual’s lack of shelter use, this study can inform the design of tailored interventions that better meet the needs of this unique population and reduce barriers to accessing emergency shelters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".