Planning for retirement during active service in Ghana: Insights from pensioners in the Greater Accra Region
Bibliographic record
Abstract
Workers often participate in pre-retirement planning activities to gain awareness of the likely changes they may experience when they retire to enable them to prepare accordingly. Although pre-retirement planning is essential for successful retirement and healthy aging, studies on pre-retirement planning activities among older adults in Ghana are limited. This study explored pre-retirement planning actions that were taken by Social Security and National Insurance Trust (SSNIT) pensioners in the Greater Accra Region of Ghana. A sequential explanatory mixed-methods approach was adopted to gather data from 437 pensioners aged 60 years and above through surveys, interviews, and focus group discussions. The results indicate that while in active service, many (309) pensioners were not motivated to plan for retirement due to issues, such as low income, and distrust of financial institutions. When planning did take place, the pensioners favored financial planning over social, mental, and physical planning. The respondents also revealed that they did not prepare adequately for retirement due to low salaries, as well as low knowledge on pre-retirement planning. Policies are needed to encourage pre-retirement planning among workers in Ghana to enable them to have an appreciable quality of life in old age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".