Bibliographic record
Abstract
BACKGROUND: This paper provides the findings from a large pilot study, Defining and Measuring Elder Abuse and Neglect, a precursor to a national prevalence study to be conducted in Canada beginning in September 2013. One purpose of this study and the focus of this paper was to determine whether a life course perspective would provide a useful framework for examining elder abuse. The two-year pilot study, 2009-2011, examined the prevalence of perceptions of abuse at each life stage by type of abuse, the importance of early life stage abuse in predicting types of elder abuse, and early life stage abuse as a risk factor for elder abuse. METHODS: Older adults who were aged ≥55 years (N = 267) completed a cross-sectional telephone survey, comprising measures of five types of elder abuse (neglect, physical, sexual, psychological, and financial) and their occurrence across the life course: childhood (≤17 years), young adulthood (18 to 24 years), and older adulthood (5 to 12 months prior to the interview date). Data analyses included descriptive statistics, bivariate correlations for abuse at the various life stages, and the estimation of logistic regression models that examined predictors of late life abuse, and multinomial logistic regression models predicting the frequency of abuse. RESULTS: Fifty-five percent of the sample reported abuse during childhood, and 34.1% reported abuse during young adulthood. Forty-three percent said they were abused during mature adulthood, and 24.4% said they were abused since age 55 but prior to the interview date of the study. Psychological (42.3%), physical (26.6%), and sexual abuses (32.2%) were the most common abuses in childhood while psychological abuse was the most common type of abuse at each life stage. When the risk factors for abuse were considered simultaneously including abuse during all three life stages, only a history of abuse during childhood retained its importance (OR = 1.81, p = 0.046, CI = 1.01-3.26). Abuse in childhood increased the risk of experiencing one type of abuse relative to no abuse, but was also unrelated to experiencing two or more types of abuse compared to no abuse. CONCLUSIONS: Results suggest that a life course perspective provides a useful framework for understanding elder abuse and neglect. The findings indicate that a childhood history of abuse in this sample had a deciding influence on later mistreatment, over and above what happens later in life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".