Experiences of Trauma for Older Adults With Lived and Living Experiences of Homelessness in Middle to High Income Countries: A Systematic Review and Meta-Aggregation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Research has identified that the number of older adults experiencing homelessness in middle to high income countries is growing. Coincidingly, in recognition that individuals with housing precarity often have experiences of trauma, there have been increasing calls for trauma-and-violence-informed care (TVIC) in practice and research. We conducted this review to consolidate existing literature that explores experiences of trauma among older adults who have experienced homelessness. RESEARCH DESIGN AND METHODS: We conducted a systematic review of qualitative evidence and meta-aggregation following the Joanna Briggs Institute methodology, in adherence with PRISMA guidelines. RESULTS: Our search yielded 24 studies. Through a process of meta-aggregation, we generated 5 synthesized findings: (a) Being let down by society and systems; (b) the world is not a safe place; (c) survivor not victim; (d) living in the long shadow of trauma; and (e) homelessness as a deeply personal trauma. DISCUSSION AND IMPLICATIONS: Our findings underscore the reality that older adults without housing face multiple experiences of trauma, including the trauma of homelessness itself. Considering these findings, research, practice, and policies need to focus on ways to better support older adults, both in preventing trauma and assisting those who have already experienced trauma. Our findings indicate the necessity of: (a) implementing TVIC across all sectors who work with older adults; (b) supporting older adults to age in place in safe, deeply affordable, accessible housing; and (c) creating shelter environments more suitable for older adults, and especially those who have experienced trauma.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".