Adult self‐reported childhood maltreatment types are associated with treatment satisfaction and alcohol relapse in patients with comorbid substance use and mental health disorders
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Individuals with comorbid substance use and mental health disorders (concurrent disorders; CD) report poor treatment outcomes, high prevalence of childhood maltreatment, and mostly negative experiences with treatment. No studies to date have examined childhood maltreatment and treatment outcomes in CD. This study investigated self-reported childhood maltreatment as it relates to treatment satisfaction and substance use relapse among CD patients. METHODS: The 258 CD inpatients completed a self-report questionnaire package, comprising the Childhood Trauma Questionnaire and the Inpatient Consumer Survey (ICS). Childhood maltreatment was assessed according to five subtypes and self-perceived treatment satisfaction was rated across six ICS domains. Psychiatric diagnoses, substance use status and relapse data were retrieved via patient medical charts. RESULTS: Emotional neglect was associated with lower ratings across all ICS domains and physical neglect was associated with a lower rating for 'outcome of care'. Childhood sexual abuse was associated with a greater likelihood of alcohol relapse. No other relationships were statistically significant. DISCUSSION AND CONCLUSIONS: The presence of childhood neglect (but not abuse) was more associated with overall treatment dissatisfaction, and sexual abuse alone increased the likelihood of alcohol relapse. These findings suggest some early adverse experiences in CD patients may increase negative experiences in treatment while others contribute to the risk of substance use. Broader longitudinal research is needed to examine the trajectory leading to negative outcomes. SCIENTIFIC SIGNIFICANCE: This is the first study to report differential patterns of association by type of childhood maltreatment on negative outcomes in treatment among CD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".