Physical Abuse of the Elderly: Results from the Abuel Study in Seven European Countries
Bibliographic record
Abstract
Introduction. The abuse of older persons is increasingly a source of concern world-wide. The abuse may involve physical, psychological and sexual aggression, injuries or financial maltreatment and neglect. Regardless of the form of abuse, it may lead to physical and mental health problems, and decreased quality of life for the older person. Notwithstanding, there is little information about the abuse of older persons. Therefore, one of the aims of ABUEL was - to investigate the prevalence of physical abuse among elderly persons in selected European countries. Methods. The data was collected cross-sectionally (“ABUEL, Elder abuse: A multinational prevalence survey”) in 2009. The respondents were 4467 randomly selected persons aged 60-84 from seven European cities: Ancona (Italy), Athens (Greece), Granada (Spain), Kaunas (Lithuania) Ludwigsburg (Germany), Porto (Portugal), Stockholm (Sweden). The various types of abuse (e.g. physical, psychological, sexual) were assessed. Results. The preliminary findings showed that almost one quarter (22.6%, CI (95%) 21.4-23.8%) of the respondents experienced any type of violence (excluding neglect) during the past year. The physical violence was reported by 2.6% of the European respondents and it corresponds to 11.6% of all reported cases of violence. This type of violence differed by country. The highest rates were identified in Sweden (4.2%) and Greece (3.4%) and the lowest in Italy (1.0%) and Spain (1.3%). Some of the physical violent acts caused serious physical injuries, with the highest rates in Lithuania (1.43%) followed by Athens, Porto and Stockholm (1.1%, 0.8% and 0.6% respectively). In contrast, Ancona’s respondents did not report any case of injuries. In dept analysis of Lithuanian data showed that physical injuries were more common among female, low educated and married respondents. Discussion and Conclusions. The preliminary results indicate [...].
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".