Overall and Gender-specific Associations between Dimensions of Adverse Childhood Experiences and Mental Health Outcomes among Homeless Adults: Associations Générales et Sexospécifiques Entre les Dimensions des Expériences Défavorables de L’enfance et les Résultats de Santé Mentale Chez les Adultes Sans Abri
Bibliographic record
Abstract
OBJECTIVE: The associations between adverse childhood experiences (ACEs) and psychopathology have been well-established in the general population. Research on ACEs in the homeless population has been limited. This study examined whether ACE exposure is associated with specific mental health outcomes among a national sample of homeless adults with mental illness and whether this association varies according to ACE dimension and gender. METHODS: This cross-sectional study utilized data from a national sample of 2,235 homeless adults with mental illness in Canada to evaluate their sociodemographic characteristics, exposure to ACEs, and mental health outcomes. Exploratory and confirmatory factor analyses were conducted to identify and confirm ACE dimensions (maltreatment, sexual abuse, neglect, divorce, and household dysfunction) from individual ACE items. Multivariable logistic regression was used to examine the associations between total ACE score and ACE dimensions with mental illness diagnoses and psychopathology severity. RESULTS: ]: 2.99). Total ACE score was positively associated with several mental illness diagnoses and psychopathology severity. Unique associations were found between specific ACE dimensions and poor mental health outcomes. The prevalence of almost all ACEs was significantly higher among women. Yet, associations between several ACE dimensions and poor mental health outcomes existed uniquely among men. CONCLUSIONS: There are unique and gender-specific associations between specific ACE dimensions and mental health outcomes among homeless adults. Better understanding of the mechanisms underlying these associations is needed to inform screening, prevention, and treatment efforts, particularly given the very high prevalence of ACEs among this vulnerable and marginalized population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".