Mediating Role of Employee Decision on Relationship Between Employee Separation Planning and Retirement Preparedness in Kenya
Bibliographic record
Abstract
Globally, majority of employees particularly from public institutions are associated with poverty during retirement despite living well during employment life. Retirement preparedness is viewed as a deliberate planning process by an individual and ought to start a long while before actual organization – employee separation. It is therefore important to relook at the concept of employee separation planning and retirement preparedness when it is mediated by employee decision which is integral to the life mastery of control that an individual exhibit. The target population was 1,238 teachers aged 50 years and above and employed in public secondary schools in Kenya by 2017. A representative sample of 334 respondents was selected using multistage sampling technique. Data was collected using semi structured questionnaire and interview guide. Logit regression was used to establish the relationships between variables in the study and to test the null hypotheses at P ≤ 0.05 and 95% confidence level. The study found that employee decision making had partial mediating effect on the relationship between employee separation planning and retirement preparedness. The study recommended the government and the employer organizations to enact frameworks that encourages and stimulates employees to make decisions to engage in programmes geared towards separation planning for successful retirement preparedness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".