Leaders' Physical and Mental Well-Being: Antecedents, Expectations and Outcomes
Bibliographic record
Abstract
Despite hundreds of articles published on leadership every year (Barling, 2014), an understanding of leaders' health has largely been neglected in the literature (Barling & Cloutier, 2016). In contrast, research on employees' mental and physical health boasts a long and robust history, attracting scientific scrutiny for almost a century (Barling & Griffiths, 2011; Christie & Barling, 2011). This presents a unique opportunity to bridge two substantial bodies of research that until recently have remained relatively separate: leadership and well-being (Barling & Cloutier, 2016). The proposed symposium considers why leaders' health has received little attention (1st presentation); how individuals transitioning into leadership positions perceive organizational resources to support mental health (2nd presentation); how non-work related antecedents of leaders' health (e.g., marital and familial relationship quality, sleep quality) can impact leadership behaviors (e.g., passive leadership, abusive supervision; 3rd and 4th presentations), and how interventions may facilitate the taxing role of leadership (e.g., mindfulness training; 5th presentation). Together, the presentations examine the antecedents to leaders' health, consider the empirical implications of leaders' health to leadership quality, and offer several directions for future research.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".