Impact of leadership development on emotional health in healthcare managers
Bibliographic record
Abstract
AIM: To examine the effects of a Leadership Development Initiative (LDI) on the emotional health and well-being among five levels of healthcare managers. BACKGROUND: Increasingly dynamic, demanding healthcare environments result in highly stressful work atmospheres. METHODS: Using quasi-experimental and mixed methods, we used regression on pre- and post-LDI survey data with 86 managers, and individual/focus group interview data for focused ethnographic analysis. RESULTS: An increasing trend was observed in self-assessed leadership practices after the LDI with a significant increase in 'inspiring a shared vision' (P<0.01). However, a non-significant decreasing trend in areas of work life and a non-significant increase in cynicism (P=0.14) was observed. Before the LDI, participants' self-assessment of their practice to 'enable others to act' was negatively related to emotional exhaustion (P<0.01). Both before and after the LDI, 'modelling the way' was significantly related to professional efficacy (P<0.01 pre; P<0.05 post). Post-LDI, 'inspiring a shared vision' was negatively (P<0.01) and 'enabling others to act' was positively (P<0.05) related to cynicism. CONCLUSION: The LDI provided opportunities for healthcare managers to connect, strengthen leadership and social support networks and manage burnout. IMPLICATIONS FOR NURSING MANAGEMENT: Transformational leadership practices may influence managers' emotional health. Senior administrative support and communicating the structure and vision of developmental initiatives may help to achieve realistic expectations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".