EVALUATION OF HOME TYPE, ABUSE PREVALENCE AND CARE-PERCEPTION ON PHYSICAL AND EMOTIONAL HEALTH AMONG ELDERLY PEOPLE IN SOUTH WEST, NIGERA
Bibliographic record
Abstract
Many countries may encounter a demographic change where the number of elderly people will increase. As a result, the number of very old people needing care, services and medical assistance will increase. Care in the private home is often described as providing the best alternative for many elderly people. The aim of this study was to evaluate elderly people’s home type, different form of abuse experienced, perceptions of how they are cared for and its effect on their physical and emotional health. The was a survey research adopting ex-post facto research design. Multistage sampling technique was used to sample Three hundred and sixty elderly people living in a private home that participated in this study. Three instruments: Elderly Persons Care-Perception Questionnaire (ECPQ), r=0.87, Elderly Abuse Prevalence Questionnaire (EAPQ) r = 0.88 and A structured Interview with inter-rater reliability of 0.93 was used to collect data. Descriptive statistics (mean and standard deviation) and Multiple Regression Analysis was used to determine possible effect among variables. The result revealed a positive and significant relationship between abuse prevalence, care-perception (? = 0.247, t (629) = 6.383, P < 0.05), Care-Perception (? = -.047, t = .55, and physical and emotional health. Furthermore, nearly a quarter or 25% reported significant levels of psychological abuse of neglect which affected their physical and emotional health. It is therefore recommended that strategies to monitor abuse among elderly people be put in place while arrangement is made for public home caregivers to reduce prevalence of abuse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".