Project Lotus: A really cool community-based initiative assisting women post-homelessness
Bibliographic record
Abstract
In Canada, recent conservative estimates report upwards of 235,000 individuals are homeless on a given night. Of those experiencing precarious housing situations, women make up approximately 30% and are among the most vulnerable. Their residential insecurity has been further exacerbated with the community and social restrictions of the COVID-19 pandemic. Existing resources that assist women experiencing homelessness or housing insecurity are often stretched to the limit dealing with emergency and crisis housing situations, with less focus on post-shelter supports. To address this issue, a community-based participatory research initiative ‘Project Lotus - Hope Together’ was established in Montreal. Grounded in the World Health Organization’s Commission on Social Determinants of Health Framework, the overarching goal of this research is to co-design a housing supports program for women leaving a shelter stay. We created a cross-sectorial Advisory Committee consisting of women with lived experiences of homelessness, service providers, community leaders, and researchers. To date, we have conducted preliminary research (literature review, interviews with women with lived experience of homelessness, stakeholder meetings) to identity what has assisted women through this transition, and what barriers exist. We have also held virtual community consultation meetings to discuss preliminary findings of recommendations of key components that should be in a post-shelter support program for women. This presentation outlines the current findings and highlights the importance of participatory research. Implementing whole person care in the area of women’s homelessness requires both a comprehensive and individualized approach to help women and children secure home, health, and a sustainable future.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".