Childhood Sexual Abuse Among New Psychiatric Outpatients in a City in Northern Alberta-prevalence Rate and Demographic/Clinical Predictors
Bibliographic record
Abstract
Child sexual abuse (CSA) is a major global health problem with serious adverse effects at later ages. Our paper examines the prevalence rates and the demographic and clinical predictors of CSA among adult psychiatric outpatients. A data assessment tool was used to compile information on the demographic and clinical characteristics of all new patients assessed in four psychiatric outpatient clinics between 1st January 2014 and 31st December 2015. The 12-month prevalence rate for CSA among new psychiatric outpatients in Fort McMurray was 20.7% (10.7% for males and 26.9% in females). With an odds ratio for sex of 3.30 (CI = 2.06–5.29), female patients are about three times more likely to report a history of CSA compared to male patients when controlling for other factors. Similarly patients with at most high school education (OR = 1.8, CI = 1.145–2.871) and those with previous contact with psychiatric services (OR = 1.7, CI = 1.124–2.616) were about two times more likely to report a history of CSA compared to the patients with college/university education or those with no previous contact with psychiatric services respectively. Similarly, patients with histories of substance abuse (OR = 1.5, CI = 1.179–2.642) and patients with family histories of mental illness (OR = 1.8, CI = 1.032–2.308) had higher likelihoods of reporting histories of CSA compared to patients without histories of substance abuse or family histories of mental illness respectively. Our findings suggest that victims of CSA are an at-risk population in need of ongoing mental health and educational support. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".