A Psychological Appraisal of Pre-Retirement Anxiety Among Some Selected Workers in Lagos Metropolis
Bibliographic record
Abstract
This study presents the findings on workers reaction to retirement in Lagos metropolis. A total number of eight hundred (800) participants (M=400, F= 400) were randomly selected from both private and public sector organisations in Lagos metropolis. The data were collected using a pre-retirement anxiety scale (PAS) developed, standardized and validated by the researcher along side with emotional intelligence and self-efficacy scales. Simple percentages, independent t test and linear regression analyses were used for analysed the data. Some of the findings include: a) Workers classified as possessing low emotional intelligence and self efficacy reported higher pre-retirement anxiety compared to their counterparts who possessed high emotional intelligence and self efficacy. b) There were inverse relationships between emotional intelligence and pre-retirement anxiety on the one hand and self-efficacy and pre-retirement anxiety on the other hand. c) Workers’ levels of emotional intelligence and self-efficacy were important predictors of pre-retirement anxiety as they both accounted for 32% variance in pre-retirement anxiety. The implications of the findings in terms of minimizing pre-retirement anxiety were discussed and recommendations were made accordingly.
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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) | 0.001 | 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".