Ethical Leadership Promotes Positive Work Outcomes Among Employees With Disabilities
Bibliographic record
Abstract
People with disabilities are more likely to be both unemployed and under-employed compared to their non-disabled counterparts (Shahidi et al., 2023), and as such it is important to focus on what may make this cohort more likely to stay, and thrive, at work. Because research supports an association between leadership and employee behaviour (Domfeh et al., 2016), and given the characteristics of ethical leadership (i.e., treat people fairly, create inclusive environments where diversity is embraced; Walumbwa & Schaubroeck, 2009), this style of leadership may be especially important when it comes to employees with disabilities. Thus, the purpose of the current research is to examine the association between ethical leadership and turnover intentions and motivation to lead among employees with disabilities. More specifically, it is proposed that loneliness will moderate the relationship between ethical leadership and turnover intentions, and that self-compassion will moderate the relationship between ethical leadership and motivation to lead. This study consisted of 223 participants, recruited through Prolific (all of whom self-identified as having a disability and working with a leader). Results suggest that ethical leadership is negatively related to turnover intentions, and that loneliness moderates the relationship between ethical leadership and employee turnover intentions. Ethical leadership was not associated with motivation to lead, and self-compassion did not moderate this relationship. Investigating the effect that ethical leaders may have on employees with disabilities is an important avenue for future research, and has implications for organizations, leaders, and employees alike.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".