Role stressors and coping strategies among nurse managers
Bibliographic record
Abstract
Purpose The purpose of this paper is to share preliminary evidence about nurse managers' (NMs) role stressors and coping strategies in acute health-care facilities in Western Canada. Design/methodology/approach A qualitative exploratory inquiry provides deeper insight into NMs' perceptions of their role stressors, coping strategies and factors and practices in the organizational context that facilitate and hinder their work. A purposeful sample of 17 NMs participated in this study. Data were collected through individual interviews and a focus group interview. Braun and Clarke's (2006) six phase approach to thematic analysis guided data analysis. Findings Evidence demonstrates that individual factors, organizational practices and structures affect NMs stress creating an evolving role with unrealistic expectations, responding to continuous organizational change, a fragmented ability to effectively process decisions because of work overload, shifting organizational priorities and being at risk for stress-related ill health. Practical implications These findings have implications for organizational support, intervention programs that enhance leadership approaches, address individual factors and work processes and redesigning the role in consideration of the role stress and work complexity affecting NMs health. Originality/value It is anticipated that health-care leaders would find these results concerning and inspire them to take action to support NMs to do meaningful work as a way to retain existing managers and attract front line nurses to positions of leadership.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".