Psychological Capital and Core Self-Evaluations in the Workplace: Impacts on Well-Being
Bibliographic record
Abstract
The uncertainty of today’s working environment, including prevalence of temporary employment conditions in many industries, has affected the psychological well-being of people in the workforce. Psychological well-being affects all aspects of a person’s life, including: pleasure, job satisfaction and fulfilment, and life meaning (Seligman, 2002). Previous studies have investigated how Psychological Capital (PsyCap) and Core Self-evaluations (CSE) are positively related to job satisfaction and performance, but there is little research on the relationships of PsyCap and CSE with psychological well-being (PWB). This present study explored the relationships among PsyCap, CSE, and PWB in a convenience workplace sample of 121 Australian working adults. Results revealed that both PsyCap (involving hope, optimism, resilience and self-efficacy) and CSE (involving evaluations of one’s own locus of control, self-esteem, generalised self-efficacy, and adaptive vs ‘neurotic’ behaviour) were separately positive predictors of wellbeing, consistent with previous studies. There were overlaps in concepts but both PsyCap and CSE together predicted higher levels of well-being than either alone, and CSE was found to be a partial mediator between PsyCap and well-being indicating that both elements were needed in prediction of well-being. Practical implications include that PsyCap and CSE measures can be used together in the workplace in assessment, selection, training and development to help improve the quality of health and well-being of employees. Limitations and future research directions are indicated.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".