The life course of homeless female Veterans: Qualitative study findings
Bibliographic record
Abstract
Introduction: Homelessness among Veterans is a significant problem in the United States, and female Veterans, one of the fastest-growing groups in the homeless population, are four times more likely to become homeless than their male peers. The purpose of this article is to share findings of a qualitative study that examined the life course that created a pathway into homelessness for 14 female Veterans in the United States. Methods: Data were collected using a life history grid and semi-structured interview guide in two 90-minute face-to-face interviews with each participant. Qualitative content analysis of the interview transcripts was conducted to identify major themes across the lifespans of study participants. Results: Six major themes shared by the participants emerged: traumatic experiences across the lifespan, entering the military to escape circumstances, racism, gender-related discrimination and sexism, difficulty transitioning from military to Veteran status, and positive childhood experiences and proud moments during military service. Discussion: The findings suggest a range of policy, housing and service needs. The study demonstrates the unique experiences of female Veterans that require gender-specific responses. The female Veterans in this study experienced exposure to multiple traumatic events pre-military, during service, and post service. Female Veterans’ increased risk of becoming homeless indicates the need for practitioners to address mental health, substance use, re-integration stability, and other health care needs and use trauma-informed interventions to ensure high-quality care. Practitioners also need to be well-versed in military and Veteran culture to provide the linkages to care and support systems required by these Veterans.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".