Elder Abuse and Neglect in Long Term Care Facilities in America : A systematic Review
Bibliographic record
Abstract
Hypothesis: Elder abuse and neglect occurs as a result of lack of relevant personnel training, poor remuneration, and depression among caregivers. To test the hypothesis, overall findings analyzed confirm if they were in agreement or not. Background: Reported cases of elder abuse and neglect have been on the rise. To add on to that, not enough studies exist that can be of help in guiding policy-makers to formulate solutions and offer answers as to why this problem is on the increase. Method: These researchers conducted a review of existing literature about elder abuse in order to better understand the risk factors and causes of elder abuse and neglect. Information was mined from various databases that contained information relevant to this review. Results: Results of findings showed that reports of abuse cases were on the rise, especially among elder patients; worse still for those with a secondary chronic illness like dementia and Parkinson’s disease. Women reported more cases of abuse compared to those reported by men. By demographics, abuse was found to be much higher among minority groups like African Americans and Asians Americans. Some elder patients in the studies experienced concurrent types or forms of abuse. Those with severe forms of cognitive impairments reported the highest cases of abuse and self-neglect. Conclusion: Better remuneration, continuous training to caregivers about aging and better healthcare skills are necessary to help to end this scourge. This study concludes that despite great efforts made by some institutions to end abuse and neglect, more publicity, more studies or research and more funds are required in order to build a sufficient body of knowledge that can be relied upon by the relevant policy-makers
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".