Processes and experiences of women after leaving a shelter: a scoping review
Bibliographic record
Abstract
After a shelter stay, women frequently face continued housing and economic instability. This scoping review, which is a companion paper to our scoping review on outcomes for women post-shelter (Jacobsen et al. [2024]. Outcomes for women after leaving a shelter: A scoping review of the quantitative evidence. Women’s Studies International Forum, 105, 102921.), explores the experiences of women post-shelter stay, and factors that affect this transition. We followed the Levac et al. ([2010]. Scoping studies: advancing the methodology. Implementation Science, 5(1), 1–9.) framework for conducting scoping reviews. After screening 6,895 articles, 37 met the inclusion criteria. Four salient themes emerged relating to the experiences of women post-shelter: connection and community, finding a place to call home, creating a new life, and caring for self. Our findings add to the existing literature to underscore the paramount importance of ontological security and engagement in meaningful activities for women transitioning from shelter to housing. This review highlights the necessity of including the voices of women to understand what safety, security, and community integration mean to them. Including the voices of women with lived experience to guide research, practice and policy is imperative to find solutions that are socially just, accessible, trauma-informed, and gender-transformative.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".