STOPPING OLDER PERSON GENDER-BASED VIOLENCE IN WOMEN 55+ THROUGH PROMISING PRACTICES: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Background Intimate partner and family violence (IPFV) disproportionately affect women of all ages, but are often understudied in older populations. Older women also face numerous barriers to reporting these types of abuse. A scoping review was undertaken to: (1) synthesize current knowledge about the utility and suitability of screening and intervention tools for older women (55+) experiencing IPFV; and (2) identify existing policy, practice, and research gaps with regard to current screening and intervention tools. Methods A comprehensive search of the databases Medline, CINAHL, PsychINFO, AgeLine, ASSIA, and Sociological Abstracts was conducted. In addition, grey literature sources were searched using Google Scholar, ISI Social Sciences Citation Index, ISI Conference Proceedings Citation Index-Social Science & Humanities, Dissertations & Theses: Full Text, Canadian Institute for Health Information (CIHI), and National Institute of Health (NIH). After screening and selection, 42 documents were included for data extraction. Results There were five major themes that emerged: (1) older women were not the specific targets of the studies/tools; (2) screening and intervention tools should address health outcomes; (3) tools identified were used or developed for some diverse populations; (4) two or more tools were used in combination; and (5) intervention tools should focus on social support and empowerment. Conclusions Older women, especially those with intersectional identities, are rarely represented in studies of IPFV, and are even less frequently the primary focus. Screening and intervention tools should address health outcomes, social support, and empowerment. Effective screening and intervention may stem from utilizing multiple tools in combination.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".