The impact of mindfulness on leadership effectiveness in a health care setting: a pilot study
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to examine the impact of mindfulness awareness practice (MAP) on mid-level health-care managers' leadership. DESIGN/METHODOLOGY/APPROACH: In total, 11 mid-level health-care managers in eastern Canada took part in an intensive weekend retreat and a follow-up webinar on mindfulness awareness. Perceived stress and leadership effectiveness were assessed pre- and post-intervention (i.e. four and eight weeks). A control group (n=10) also completed the same measures twice. Additionally, informants (n=28) provided assessments of participants' leadership pre- and post-intervention. Follow-up interviews were carried out with eight participants 12-16 weeks post-intervention. FINDINGS: In comparison to controls, retreat participants showed significant increases in mindfulness and corresponding decreases in stress that were sustained across eight weeks post-retreat; retreat participants reported significant positive changes in their leadership effectiveness that were corroborated by informants. Qualitative data, however, suggest that sustaining a mindfulness practice presents significant challenges to middle managers in a health care setting. RESEARCH LIMITATIONS/IMPLICATIONS: The findings are useful to management working in health services that are plagued by increasing demands and changes. Despite the small sample and lack of random assignment, the pilot data support the efficacy of MAP in improving leadership. ORIGINALITY/VALUE: Little empirical research supports the claim that MAP enhances leadership. The present study employed a mixed methods approach to address this gap and demonstrates the potential benefits of MAP among mid-level managers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".