Exploratory Study of the Clinical Characteristics of Adolescent Girls with a History of Physical or Sexual Abuse Consulting in a Mood Disorder Clinic.
Bibliographic record
Abstract
OBJECTIVE: To examine the clinical characteristics of adolescent girls consulting in a mood disorder clinic with a history of physical or sexual abuse. METHOD: A retrospective review was conducted of the charts of 55 adolescent girls consulting in a mood disorder clinic. An analysis grid was used to gather data on demographics, personal antecedents, symptoms and diagnoses. Girls with a history of physical or sexual abuse were compared with girls without such a history. Univariate analyses and a logistic regression analysis were performed. RESULTS: Adolescent girls with a history of physical or sexual abuse did not differ statistically from those without such a history in terms of depressive symptoms or type and number of diagnoses. However, proportionally more girls with a history of physical or sexual abuse presented self-harm and relational problems with their parents and peers. CONCLUSION: Both history of physical or sexual abuse and self-destructive behaviors are rooted in relational problems. The results show that these are related to one another among those adolescent girls. Clinically, these findings suggest that it is important for clinicians do a thorough exploration of self-destructive behaviors and family and peer relations when assessing depressed adolescent girls.
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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 | 0.003 |
| 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) | 0.001 | 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".