Reclaimed Voices: The Silent Impact of Women’s Experiences of Homelessness
Bibliographic record
Abstract
Background: Homelessness is a rising crisis that affects hundreds of thousands of individuals in America; but affects women differently than men. The experiences women face while homeless can translate into traumatic experiences and profoundly shape an individual's story of homelessness. These experiences prompt a need to change the systems that contribute to homelessness. Purpose: This study aims to highlight trauma across women's experiences of homelessness. Methods: Data collection for this research was conducted through qualitative interviews with eight women experiencing homelessness in Modesto, California. NVivo, a qualitative data analysis software, was used to format the interviews, identifying common code words that developed into overlapping themes. Results: The eight interviews of women experiencing homelessness identified core emerging themes of causes of homelessness, fear of loneliness, quality, and type of available services, perception of homelessness, and an understanding of the societal constructs of homelessness and gender. This research confirmed that some women experiencing homelessness are less likely to report traumatic experiences and seek support from their local social service community providers. The limitations of the smaller sample size only captured a small scale of the problem being analyzed to a potentially larger issue. Conclusion: Services should begin focusing on asking more trauma-informed questions, not only to improve care but to allow more women experiencing homelessness the chance to seek help; this is the only way to begin understanding how their traumatic experiences have impacted their livelihood.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".