Bibliographic record
Abstract
Over the last 20 years, increased public and political awareness has developed alongside research, policy and professional developments to advance our understanding of the abuse of older people in families, communities, hospitals and institutional settings. In 2002, the World Health Organization (WHO) argued that elder abuse was a distinct social problem, defining abuse as ‘a single, repeated act or lack of appropriate action, occurring within any relationship where there is an expectation of trust which caused harm or distress to an older person’ (WHO, 2002). In 2007, the first United Kingdom (UK) prevalence study of elder abuse reported that 4% of older people living in the community were subject to abuse or neglect (O’Keefe et al, 2007). In 2010, a prevalence study of elder abuse in Germany, Greece, Italy, Lithuania, Portugal, Spain and Sweden found that 19.4% of older people aged 60– 84 years were exposed to psychological abuse; 2.7% to physical abuse; 0.7% to sexual abuse; and 3.8% to financial abuse (Soares et al, 2010). Pillemer et al (2016), in reviewing the international literature, found that, globally, elder abuse prevalence rates for older people living in the community, encompassing all forms of abuse, ranged from 2.2% to 36.2%, with a mean of 14.3%. The highest combined prevalence was reported in China (36.2%) and Nigeria (30%), followed by Israel (18.4%), India (14%), Europe (10.8%), Mexico (10.3%), United Sates (9.5%) and Canada (4%). As is apparent from the wide diversity in prevalence rates noted here, determining the extent of abuse is problematic, with prevalence studies often utilising differing definitions of abuse, different target populations and different methods (Cooper et al, 2008; Pillemer et al, 2016). However, it is widely recognised that elder abuse is increasing and that it often goes unreported, with official numbers most likely underestimating the extent of the problem (Iborra, Garcia and Grau, 2013). A developing social problem The abuse of older people is understood within a broader spectrum of family violence (Phelan, 2020) in which, as discussed in Chapter 8, an ecological systems approach (Bronfenbrenner, 2005) is generally adopted.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".