Contrasting mental health correlates of physical and sexual abuse-related shame.
Bibliographic record
Abstract
OBJECTIVE: This study represents an initial attempt to contrast behavioural and mental health correlates of shame as a result of physical abuse (PA) and sexual abuse (SA). Because they are distinctive forms of injury, it is possible that corollary shame from these injuries follows unique trajectories and ultimately results in different health challenges. METHOD: Self-report data from a survey on the health of youth receiving protective services for reasons of PA and SA was used. It included standardised measures, such as the Childhood Trauma Questionnaire, Trauma Symptoms Checklist for Children, the Brief Symptoms Inventory, the Rutgers Alcohol Problem Index, and the South Oaks Gambling Screen. New measures of abuse-related shame, maltreatment, and substance use were also employed. Linear regression analyses were performed to determine whether level of shame was linked to mental health and behaviour issues, after controlling for level of abuse. RESULTS: = 0.05). CONCLUSIONS: Keeping in mind that this was largely a cross-sectional study and that causality cannot be inferred, the results seem to indicate that youth suffering from abuse-related shame are particularly vulnerable to mental health problems, but not to efforts to numb their problematic thoughts and feelings through gambling and substance use. Shame could serve as an early indicator of which child protection recipients are most in need of preventive efforts.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".