Factors associated with elder abuse and neglect in rural Uganda: A cross-sectional study of community older adults attending an outpatient clinic
Bibliographic record
Abstract
BACKGROUND: Elderlies are vulnerable to abuse, and evidence suggests that one in three elderlies experience abuse. Abuse can impact the well-being of older persons, decreasing their quality of life, leading to mental health challenges, and increasing morbidity and mortality rates. Evidence on older person/elder abuse and neglect is vital to facilitate initiatives, but there are fewer studies on elder abuse and neglect in Africa, particularly in Uganda. Thus, this study aimed to determine the prevalence of different types of abuse and neglect, and their associated factors among older persons (aged 60 years and above) attending an outpatient clinic. METHODS: In this cross-sectional study, information on sociodemographic characteristics, functional impairment using the Barthel Index, and elder abuse severity using the Hwalek-Sengstock Elder Abuse Screening Test were collected. In addition, types of abuse were assessed using questions adapted from the US National Research Council on elder mistreatment monograph. Linear and logistic regression analyses were used to determine the factors associated with elder abuse severity and the different types of abuse, respectively. RESULTS: Overall, the prevalence of elder abuse was 89.0%. Neglect was the most common type of elder abuse (86%), followed by emotional abuse (49%), financial abuse (46.8%), physical mistreatment (25%), and sexual abuse (6.8%). About 30.4% of the abused elders experienced at least two forms of abuse. Factors associated with elder abuse severity were having a secondary level of education and physical impairment. Moderate to severe functional dependence was associated with almost all forms of abuse. Individuals who reported the presence of a perpetrator were likely to experience neglect, emotional, and physical mistreatment. However, those who reported their perpetrators to the police had a higher likelihood of experiencing emotional abuse but were less likely to experience financial abuse. Emotional abuse was also associated with age above 80 years and attaining education (primary and secondary). Physical impairment and chronic medical conditions reduced the likelihood of experiencing neglect and financial abuse, and physical abuse, respectively. CONCLUSIONS: Uganda has a high prevalence of elder abuse and neglect. There is a need to design interventions for older adults at risk to prevent elder abuse from escalating further, where the present findings can be worthy of help.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".