The Post Retirement Adjustment Challenges Confronting Local Government Employees in Ethiope East Local Government Area of Delta State
Bibliographic record
Abstract
The study examined the post retirement adjustment challenges confronting local government retirees in Ethiope East Local government area of Delta State. The study adopted survey in the form of a descriptive study in which data will be collected once across a population through sampling. The research design of this study is a survey based on a structured questionnaire. The study revealed among other things that delay in the payment of retirement benefits represent the highest percentage of post-retirement challenges often faced by local government retirees in Ethiope West local government area of Delta State. Another major post retirement challenges were the absence of a social policies for retirees. It was further discovered that aging/stereotype feeling of neglect, Physical trauma and anxiety arising from absence of income generating activities is a major challenge confronting retirees in Ethiope West local government area of Delta State. Based on the findings of the study, the following recommendations are hereby outline: The Local government Service Commission should establish a mechanism that would be in cooperate with pre-retirement counseling services as well as general retirement issues to equip its employees with the basic knowledge when concerning retirement. Second, retirement planning should begin early in the employees life so that they could save enough before retirement. In this regards, employees should open retirement savings accounts for pension fund administrators of their choice to enable them to save towards their retirement. Third, government should also initiate a social policy for the aged in the society to help cushion the inadequacies often suffered by retirees in the retirement benefit management in Nigeria. More so, the new pension system should also ensure transparent and efficient management of pension funds. Furthermore, employees should be encouraged to develop a savings culture. Above all, government should strengthen the regulatory and supervisory framework and empower it to successfully and effectively check earring pension fund administrators in the country. Finally, employees and retirees should be encouraged to invest in assets and financial instruments so that at retirement they can earn additional income from these assets and financial instruments to supplement their pension income at retirement.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".