Violence, sexual abuse and health in Greenland
Bibliographic record
Abstract
The purposes of the study were to analyse the lifetime prevalence of violence and sexual abuse among the Inuit in Greenland and to study the associations between health and having been the victim of violence or sexual abuse. Associations were studied with specific attention to possible differences between women and men. Further, response rates were analysed specifically in order to understand consequences of including questions on violence and sexual abuse in the questionnaire survey. The analyses were based on material from a cross-sectional health interview survey conducted during 1993-94 with participation from a random sample of the Inuit population in Greenland (N = 1393). The prevalence of ever having been a victim of violence was 47% among women and 48% among men. Women had more often than men been sexually abused (25% and 6%) (p < 0,001) and had more often been sexually abused in childhood (8% and 3%) (p = 0.001). Having been the victim of violence or sexual abuse was significantly associated with a number of health problems: chronic disease, recent illness, poor self-rated health, and mental health problems. The associations between having been the victim of violence or sexual abuse and health was stronger for women than for men. It is possible to secure a reasonably high response rate in a general health survey that includes questions on violence and sexual abuse.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".