Homeless Canadians’ Perspectives on Homelessness in Calgary
Bibliographic record
Abstract
Since the 1990s, homelessness has increased in Canada, but the strategies of the government and public health service providers to manage the situation have had limited success. Researchers have also noted the lack of inclusion of those experiencing homelessness in homelessness research to better understand and develop a solution to the issue. In the present study, this is addressed through inclusion of homeless participants from diverse backgrounds. The purpose of this phenomenological study, framed by social cognitive theory, reciprocal determinism, and symbolic interaction, was to understand homelessness from the perspectives of people who do not have homes. Data were collected from open-ended interviews with a purposeful sample of 15 individuals who were homeless. Several themes emerged after interview data were transcribed via hand coding and analyzed using cognitive data analysis. The prominent themes were lack of money, home, privacy, and support; discrimination directed primarily toward First Nations people and those of African descent; mental illness and addiction; the need for a review of housing policy that addresses rent, mortgage qualification criteria, and house tax; and the creation of awareness of government support systems and the services that they provide. Public health service providers and designated authorities can use the findings of this study to understand the phenomenon from the perspective of people who are experiencing homelessness, which can influence the development of better homelessness reduction strategies that could improve the lives of those experiencing homelessness and their communities. Because homelessness is a public health issue, bringing it under control could positively impact the health and safety of the public.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".