Self-Esteem and Adjustment to Retirement Among Retiree Teachers in Meru County, Kenya
Bibliographic record
Abstract
Retirement poses psychological problem for many retirees. There is an increase in the number of retired teachers in Meru County in Kenya because majority of the pioneer teachers in Meru County have begun retiring since independence. It was necessary to establish the relationship between self-esteem and adjustment to retirement. The researcher established a relationship between self-esteem and adjustment to retirement among retiree teachers in Meru County. The study adopted descriptive survey research design. The study sampled 318 respondents from a population of 1800 comprising of 600 retirees, 600 spouses of retirees and 600 close relatives of the retiree teachers. A sample of 318 respondents was selected to participate in the study. Data was collected from retiree teachers and their spouses through questionnaires. Interview schedules were used to collect data from retiree teacher close relative. Quantitative data was analyzed with the help of Statistical Package for Social Science (SPSS) version 23. Descriptive statistics included frequencies, percentages, standard deviation and mean. Inferential statistics used was wilcoxon signed rank test. Qualitative data collected was analyzed by classifying the responses into meaningful categories thematically. The findings of this study revealed that there is a significant relationship self-esteem and adjustment to retirement. The study recommends that Teacher Service Commission to organize pre-retirement training and counseling to prepare teachers to handle challenges that may affect their self-esteem in retirement. DOI: 10.7176/JRDM/68-05 Publication date: August 31 st 2020
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".