CHILD ABUSE AND NEGLECT AND THE MENTAL HEALTH OF ADULT SURVIVORS: A RETROSPECTIVE STUDY
Bibliographic record
Abstract
Several researchers have indicated that child abuse and neglect are related to mental health problems in adulthood. Social support has been found to have a protective influence on the sequelae of these traumatic interpersonal experiences. The purpose of this study was to explore the relationship between the severity of childhood interpersonal trauma and the severity of mental illness and substance abuse problems in adulthood. This study is guided by the Neuman Systems Model, specifically the impact of stressors, reaction to stressors, and the moderating role of the sociocultural variable in the flexible line of defense. This research takes advantage of a large existing database from the 2005 Community University Research Alliance (CURA) on Housing and Mental Health. The community sample includes 190 participants living in Southwestern Ontario. The quantitative data collected through the Childhood Trauma Questionnaire, Personal Resource Questionnaire, and the Colorado Client Assessment Record were analyzed. Descriptive analyses were conducted to describe the sample. Inferential analyses were performed using Pearson’s r correlations and t-tests. Results revealed that the severity of interpersonal trauma experienced in childhood was related to the severity of mental illness and substance abuse problems in adulthood. The moderating role of social support, however, was not supported in this study. It is expected that the study findings will be used as a means to increase knowledge and improve practice regarding the experience of interpersonal trauma in childhood and mental health problems in adulthood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".