Subjective Well-Being of Retired Teachers: The Role of Psycho-Social Factors
Bibliographic record
Abstract
The present study was aimed to investigate the relationship between personality hardiness, social support,religiosity and loneliness purpose in life and subjective well-being in Retirees. The sample comprised 100 retireduniversity teachers from Himachal Pradesh (50 males and 50 females). The analysis revealed that for the totalsample, Purpose in Life (32%), Social Support (12%) and Religiosity (5%) have contributed 49% of variance intotality. In Males sample, Hardiness explained the maximum variance (27%) followed by Purpose in Life (14%),Social support (8%) and Religiosity (4%). In all, these variables have accounted for 53% of variance. In Femalessample, Religiosity contributed the maximum variance (32%) followed by Social support (18%) and Purpose inlife (8%). The results have shown the commonness of three variables viz., social support, purpose n life andreligiosity in predicting the subjective well-being of both the genders. Further, t-test has revealed the superiorityof females in subjective well-being, religiosity and social support and male’s superiority on hardiness, andpurpose in life. No significant difference was observed on the variable of loneliness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".