Transformational leadership and employee psychological well-being: A review and directions for future research.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it