“Too Grey To Be True?” Sexual Violence in Older Adults: A Critical Interpretive Synthesis of Evidence
Bibliographic record
Abstract
Sexual violence (SV) is an important public health issue with a 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 currently used frameworks in public health research. To fill this gap in the literature, we applied a Critical Interpretative Synthesis (CIS) approach. The CIS approach uses techniques from grounded theory and processes from systematic review. It allows to critically interpret key findings from both academic as well as grey literature, engendering theory refining. In the first phase of purposive sampling, we conducted a systematic review of academic sources and included 14 references. The cut-off age used to define old age varied between 60 and 70 years old among the included studies. Subsequently we added another 14 references in the second phase of theoretical sampling. We ultimately included 16 peer-reviewed articles and 12 documents from the grey literature. The CIS results demonstrate that knowledge of SV in older adults is still limited. The current research suggests that SV in older adults rarely occurs, however, prevalence rates are likely to be underestimated because of methodological shortcomings. The complexity of SV in older adults is not acknowledged in ongoing research due to the conflation of SV with other types of violence. Information on specific risk factors and about assailants committing SV in old age is absent. Policy documents dealing with sexual and reproductive health, rights and ageing make no mention of SV in older adults. In clinical practice, the sexual health needs of older adults often remain unmet. In conclusion, our findings suggest that older adults are forgotten in prevention and response to SV. 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 | 0.293 | 0.554 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.039 | 0.021 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| 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".