Prevalence and Nature of Sexual Violence in Older Adults in Europe: A Critical Interpretive Synthesis of Evidence
Bibliographic record
Abstract
ABSTRACT Sexual violence (SV) is an important public health issue with a potential major impact on victims and their peers, offspring and community. However, SV in older adults is under-researched. This paper aims to establish the prevalence and nature of SV in older adults in Europe, link this with existing policies and health care workers’ response to sexual health needs in older age and critically revise the current used frameworks in public health research. We applied a Critical Interpretative Synthesis. After the first phase of purposive sampling we included 14 references. Another 14 references were included after the second phase of theoretical sampling. We ultimately included 16 peer-reviewed articles and 12 documents from the grey literature. 0.0% to 3.1% of older adults in Europe were sexually victimised in the past year. Lifetime prevalence of SV was 6.3%. Information on specific risk factors and assailants committing SV in old age is non- existing. Although in theory policy makers increasingly recognise the importance of sexual health in older age, SV in older adults is not mentioned in policy documents on sexual and reproductive health and rights and ageing. In clinical practice, the sexual health needs of older adults remain often unmet. Knowledge about SV in older adults is still limited. Ongoing research does not fully grasp the complexity of SV in older adults. Greater awareness about this topic could contribute to a revision of current policies and health care practices, leading to more tailored care for older victims of SV.
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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 | 0.112 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.032 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| 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".