Elder abuse: the hidden face of domestic violence
Bibliographic record
Abstract
Introduction Violence against the elderly constitutes an undeniable and serious violation of human rights and affects the physical and psychological integrity of the victim. Is not a new phenomenon, is a worldwide problem that has become more pronounce in contemporary societies because of the ageing of the population. World Health Organization [1 World Health Organization (WHO). The Toronto declaration on the global prevention of elder abuse. Geneva: World Health Organization; 2002. [Google Scholar]] defines elder violence as a single or repeated action, or the absence of an appropriate action, arising in the context of a relationship where there is an expectation of trust that causes suffering or harm to an elderly person. Occurs through several behaviours involving psychological, physical, sexual, financial violence, neglect and self-neglect [2 National Research Council (NRC). Elder mistreatment: abuse, neglect, and exploitation in an aging America. Washington, DC: The National Academies Press; 2003. [Google Scholar]]. The purpose of this paper is to demonstrate the work developed by the Victims Information and Assistance Office (GIAV) and by Forensic Psychology Office (GPF) at Egas Moniz Higher Education School about elder abuse.Materials and methods The sample (n = 14) is derived from the domestic violence risk assessments of GIAV and GPF. We assessed 6 victims: 2 women and 4 man, aged between 64 and 95 years old (M = 76.67, sd = 10.71); and 8 defendants: 6 women and 2 man, aged between 24 and 77 years old (M = 46.13, sd = 15.52). The relationship between victims and defendants are 13 sons/daughters and 1 tenant. Data were collected from lawsuits, semi-structured interviews of the victims and defendants, collateral information and criminal record. All ethical issues have been taken due to the sensitive nature of the involved data involved and the respective informed consentient which contained the purpose of the assesses, the confidentiality limits, and information about the ethics and technician’s impartiality was sign by all participants.Results The results demonstrated physical and psychological abuse (in all cases), followed by economical abuse (n = 13, 92.9%) and social abuse (n = 3, 21.4%). It is possible to identify several victims’ risk factors, namely gender (female victims – n = 11, 78.6%), physical problems/limitations (n = 11, 78.6%), age above 75 years old (n = 8, 57.1%) and previous abuse (n = 6, 42.9%). The most relevant offender’s risk factors are financial problems (n = 12, 85.7%), deficit in the coping skills (n = 12, 85.7%), others blame (n = 10, 71.4%), history of violence against others (n = 8, 57.1%), aggressiveness (n = 8, 57.1%), criminal history (n = 6, 42.9%), victim of domestic violence in the past (n = 8, 35.7%) and perpetrator of domestic violence in the past (n = 5, 35.7%). Finally external/relational factors are: offender’s dependence (n = 11, 78.6%), cohabitation (n = 11, 78.6%), history of conflicts between victim and offender (n = 10, 71.4%), poor emotional attachment or low family cohesion (n = 10, 71.4%), social isolation or lack of social support (n = 8, 57.1%), intergenerational transmission of violence (n = 7, 50%), inability in the performance of caregiver tasks (n = 5, 35.7%) and inexperience as caregiver (n = 5, 35.7%).Discussion and conclusions Portugal it’s one of the top five European Countries with higher percentage (39%) of elderly mistreated [3 Associação Portuguesa de Apoio à Vítima. Pessoas idosas vítimas de crime e violência 2013/2017. Lisboa: APAV; 2018. [Google Scholar]], however, elder abuse is still the hidden face of domestic violence. The data show several risk factors for elder abuse. These results demonstrated the urgency about elder abuse risk assessment in criminal justice system and the need of a good articulation between Forensic Psychology and Law in order to demystify the hidden face of elder abuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".