Transformational leadership and employee psychological well-being: A review and directions for future research.
Bibliographic record
Abstract
This review paper focuses on answering 2 research questions: (a) Does transformational leadership predict employee well-being? (b) If so, how and when does this prediction occur? A systematic computerized search and review of empirical papers published between January 1980 and December 2015 was conducted. Forty papers were found that met the criteria of reporting empirical results, being published in English, and focused on answering the above research questions. Based on these papers it appears that, in general, transformational leadership positively predicts positive measures of well-being, and negatively predicts negative measures of well-being (i.e., ill-being). However, recent findings suggest that this is not always such a simple relationship. In addition, several mediating variables have been established, demonstrating that in many cases there is an indirect effect of transformational leadership on employee well-being. Although some boundary conditions have been examined, more research is needed on moderators. The review demonstrated the importance of moving forward in this area with stronger research designs to determine causality, specifying the outcome variable of interest, investigating the dimensions of transformational leadership separately, and testing more complicated relationships. (PsycINFO Database Record
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".