Adverse childhood experiences and related outcomes among adults experiencing homelessness: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) are strong risk factors for homelessness and poor health and functioning. We aimed to evaluate the lifetime prevalence of ACEs and their associations with health-related and functioning-related outcomes among homeless adults. METHODS: In this systematic review and meta-analysis, we searched from database inception to Nov 11, 2020, for original and peer-reviewed studies in English that documented lifetime prevalence of ACEs or associations between ACEs and health-related or functioning-related outcomes. We selected studies if they included a definable group of homeless adults and measured at least four ACE categories. We calculated pooled estimates of lifetime prevalence of one or more ACEs and four or more ACEs with random-effects models. We used the leave-one-out method in sensitivity analyses and studied meta-regressions to explore potential moderators of ACE prevalence. We also did a narrative summary of associations between ACEs and health-related and functioning-related outcomes, as there were too few studies on each outcome for quantitative meta-analysis. This study is registered with PROSPERO, CRD42020218741. FINDINGS: >95%). Of the potential moderators analysed, the ACE measurement tool significantly moderated the estimated lifetime prevalence of one or more ACEs and four or more ACEs, and age also significantly moderated the estimated lifetime prevalence of four or more ACEs. In the narrative synthesis, ACEs were consistently positively associated with high suicidality (two studies), suicide attempt (three studies), major depressive disorder (two studies), substance misuse (two studies), and adult victimisation (two studies). INTERPRETATION: The lifetime prevalence of ACEs is substantially higher among homeless adults than among the general population, and ACE exposure might be associated with prevalence of mental illness, substance misuse, and victimisation. Policy efforts and evidence-based interventions are urgently needed to prevent ACEs and address associated poor outcomes among this population. FUNDING: Rhodes Trust and Canadian Institutes of Health Research.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".