Virtues, work experiences and psychological well‐being among managerial women in a Turkish bank
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationship between virtues and indicators of work engagement, satisfaction, and psychological well‐being among a large sample of managerial and professional women working in a large Turkish bank. Managerial women in Turkey, as elsewhere, are under‐represented at senior levels of management. A virtue is any psychological process that enables a person to benefit herself or himself and others. Design/methodology/approach Data are collected from 286 managerial and professional women using anonymously completed questionnaires, a 72 percent response rate. Two virtues are considered: Optimism and Proactive Behavior. Findings Optimism and Proactive Behavior are significantly and positively correlated. Hierarchical regression analyses, controlling for both personal demographic and work situation characteristics, indicate that virtues account for significant increments in explained variance on all outcome measures. Optimism emerges as a particularly consistent predictor of these. Research limitations/implications The research data are collected at one point in time, limiting the understanding of causality. Practical implications Suggestions for increasing levels of virtues through training are offered based on previous theory. Originality/value This paper contributes to the emerging literature in positive organizational scholarship on the relationship of virtues to individual health and performance in work settings.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".