The Challenges Militating Against the Adoption of Pre-retirement Training of Retirees in Nigeria
Bibliographic record
Abstract
The main thrust of this paper is to give an overview of the challenges faced by retirees in in selected Federal Teaching Hospitals in Nigeria after retirement. Data for this study were collected from both primary and secondary sources. A total number of 337 questionnaire were administered representing 20% of the total population of 1684 using probability proportion to size technique was used in order to have balanced views of the respondents and the data collected were analysed using descriptive statistics. Results from the study showed that Nine (9) possible challenges which were presented to the respondents in the questionnaire. These challenges are: Poor funding of the training programme; apathy on the part of trainees; corruption/ mismanagement of funds for the training programme; inadequacy of data on retirees fot the training programme; inadequate Civil Society Organisations’ participation in the programme; absence of managerial skills by retirees to cope with the demands of entrepreneurship; poor coordination of the training programme; and misappropriation of funds from gratuities into other uses by retirees. This study therefore recommends that government should provide more funds; be more committed to the programme; intending retirees should be well informed about the treining programme; timing of pre-retirement training should come should come early at least twice between 7-10 years as against 3-5 years before retirement. All these will enable the intending retirees to prepare for obvious eventuality after retirement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".