MISTREATMENT ACROSS THE LIFE COURSE AS A MAJOR PREDICTOR OF ELDER ABUSE: A CANADIAN NATIONAL STUDY
Bibliographic record
Abstract
Few researchers have claimed that abuse at an earlier stage in life may be a risk factor for elder abuse later in life. Indeed, the elder abuse literature frequently highlights that the social learning model causes abuse in later life, the argument being that abusers learn how to be violent from witnessing or suffering from violence. The aim of this research was to test the hypothesis that if an older person was abused earlier in their lives, they were more likely to be abused as older adults. A national telephone survey was conducted to estimate the prevalence of five forms of elder abuse in community dwelling older Canadians who were 55 years and older. A representative, stratified sample of 8,163 Canadians completed the survey, the largest study to date. Information was collected about socio-demographic factors, health, wealth, risk factors for abuse, and prevalence for the usual five subtypes of abuse. Unlike other prevalence studies, a life course perspective was the guiding theoretical framework. The analyses included descriptive statistics about the sample, bivariate analyses correlating the risk factors with abuse and a logistic regression model with the main the predictors of abuse. The results showed, in order of importance, higher depression scores as measured on the C-DES, having been abused as an adult (25–54), a child (1–17) a youth (18–24), having higher unmet ADL/IAD needs, not feeling safe with those closest to respondent, geographical location; being single compared to being married and lastly, being female were significant (p ≥ .001) predictors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".