Impairment and Abuse of Elderly by Staff in Long-Term Care in Michigan: Evidence From Structural Equation Modeling
Bibliographic record
Abstract
Elder abuse in long-term care has become a very important public health concern. Recent estimates of elder abuse prevalence are in the range of 2% to 10% (Lachs & Pillemer, 2004), and current changes in population structure indicate a potential for an upward trend in prevalence (Malley-Morrison, Nolido, & Chawla, 2006; Post et al., 2006). More than 20 years ago, Karl Pillemer called for sociological research on patient maltreatment in nursing homes and provided an overview model for the conduct of such research (Pillemer, 1988). The research literature since then has not provided the definitive model to account for patient maltreatment that Pillemer hoped for. Instead, it has produced a laundry list of risk factors that includes the patient's functional disability, cognitive impairment, social isolation, age, race, income, family background, life events, dementia, and depression (Dyer, Pavlik, Murphy, & Hyman, 2000; Lachs & Pillemer, 2004; Lachs,Williams, Obrien, Hurst, & Horwitz, 1997; Pavlik, Hyman, Festa, & Dyer, 2001; Schofield & Mishra, 2003). However, no theory exists to place these factors in a causal structure that relates the factors to each other and to whether abuse occurs. This study is a first step in that direction. Nine hypotheses were generated focusing on the effects of two dimensions of impairment--(a) physical and cognitive and (b) age and behavior problems--on susceptibility to abuse among elderly in long-term care.The relationships between factors and from factors to susceptibility to abuse are specified in a structural equation model where "susceptibility to abuse," "physical impairment," and "cognitive impairment" are latent variables, and behavior problems and age are directly measured.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".