Individual- and Relationship-Level Factors Related to Better Mental Health Outcomes following Child Abuse: Results from a Nationally Representative Canadian Sample
Bibliographic record
Abstract
OBJECTIVE: Child abuse can have devastating mental health consequences. Fortunately, not all individuals exposed to child abuse will suffer from poor mental health. Understanding what factors are related to good mental health following child abuse can provide evidence to inform prevention of impairment. Our objectives were to 1) describe the prevalence of good, moderate, and poor mental health among respondents with and without a child abuse history; 2) examine the relationships between child abuse and good, moderate, and poor mental health outcomes; 3) examine the relationships between individual- and relationship-level factors and better mental health outcomes; and 4) determine if individual- and relationship-level factors moderate the relationship between child abuse and mental health. METHOD: Data were from the nationally representative 2012 Canadian Community Health Survey: Mental Health ( n = 23,395; household response rate = 79.8%; 18 years and older). Good, moderate, and poor mental health was assessed using current functioning and well-being, past-year mental disorders, and past-year suicidal ideation. RESULTS: Only 56.3% of respondents with a child abuse history report good mental health compared to 72.4% of those without a child abuse history. Individual- and relationship-level factors associated with better mental health included higher education and income, physical activity, good coping skills to handle problems and daily demands, and supportive relationships that foster attachment, guidance, reliable alliance, social integration, and reassurance of worth. CONCLUSIONS: This study identifies several individual- and relationship-level factors that could be targeted for intervention strategies aimed at improving mental health outcomes following child abuse.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".