Discourses of spirituality and leadership in nursing: a mixed methods analysis
Bibliographic record
Abstract
AIM: To explore nursing discourses of spirituality and leadership. BACKGROUND: Global migration has brought unprecedented plurality to modern societies, and spirituality and religion into the purview of nurse leaders. METHOD: An innovative mixed methods approach, including a literature review, qualitative research and philosophic analysis, was utilized to examine discourses of spirituality in contexts of nursing leadership. After a literature synthesis protocol, 38 nursing literature sources were reviewed. Two qualitative studies examining plurality in hospital and home health settings provided data from 13 nurse leaders. Philosophic inquiry added further depth and uncovered important underlying assumptions. RESULTS: Integrated analysis revealed a heterogeneous discourse in the nursing literature. Nurse leaders in the qualitative study evidenced awareness of the influence of spirituality and concern for inclusive health services, yet were cautious in integrating spirituality into leadership practices because of organisational and social influences. Assumptions regarding the role of leaders' spiritual values and the integration of spirituality into the workplace were revealed. CONCLUSION: Spirituality in nursing leadership is a relatively understudied field that is influenced by many contextual factors. IMPLICATIONS FOR NURSING MANAGEMENT: Scholarly engagement and research are needed to analyse the grounds for and appropriate approaches to the integration of spirituality in nursing leadership. Nurse managers are positioned to facilitate this process in their organisations.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".