Factors affecting adjustment to retirement among retirees’ elderly persons
Bibliographic record
Abstract
Background and aim : The loss of work through retirement is one of the major adjustments for individual ages. For many, this is the first indicator of the impact of aging. Aim of the study was to identify factors affecting adjustment to retirement among retirees' elderly persons. Method : Cross-sectional descriptive design was adopted. This study was conducted at Waiting Lounge of El Ahly Bank, Nasser Bank, and Faculties of Commerce, Medicine, Science and Veterinary affiliated to Mansoura University, Dakahlia Governorate, Egypt. 210 retirees' elderly person who represented the participants of the current study retired since one year and more. Data was collected using Self-Administered Questionnaire, Retirement Adjustment Scale, and Retirement Resources Inventory. Results: None of the retirees' elderly (100.0%) attended a preparation program for retirement. The total mean score of retirement adjustment scale correlated significantly and positively to the total mean score of the physical, financial, social, and mental resources. Additionally, there is a significant relation between preparation for retirement and the total mean score of retirement adjustment scale. Conclusion: It can be concluded that adjustment to retirement was affected by the retirees’ gender, marital status, level of education, type of job before retirement, job condition, and place of work. In addition, adequate resources as physical, financial, social support and mental capacity are associated with better adjustment to 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".