Staying but Struggling: A Concept Analysis of Quiet Quitting in Nursing Practice
Bibliographic record
Abstract
AIM: To clarify the concept of quiet quitting in nursing practice. DESIGN: Concept analysis using Walker and Avant's concept analysis methodology. METHODS: The eight-step method by Walker and Avant guided the concept analysis. DATA SOURCES: A systematic literature search was conducted in CINAHL, PsycINFO, Scopus and MEDLINE without date restrictions, identifying 36 empirical and theoretical articles published in English. RESULTS: Quiet quitting in nursing is defined by four key attributes: minimal compliance with job expectations, psychological and emotional detachment, withdrawal of discretionary effort and lingering in role despite dissatisfaction. Antecedents include unhealthy work environments, psychosocial strain (e.g., burnout, moral distress) and individual/demographic influences (e.g., age, coping strategies). Consequences include impaired team dynamics, reduced care quality and organisational decline and increased turnover intentions. Twenty-five studies used validated measurement tools, notably the Quiet Quitting Scale. CONCLUSION: Quiet quitting is a subtle form of disengagement distinct from burnout and turnover. It reflects an adaptive coping response to sustained dissatisfaction and unmet expectations. It is both widespread and underrecognized, with implications for healthcare sustainability. IMPLICATIONS FOR PROFESSIONAL AND CLIENT CARE: Understanding and addressing quiet quitting is essential for safeguarding professional standards, promoting nurse engagement and ensuring high-quality patient care. Early identification and systemic reforms are critical to mitigating its impact. IMPACT: This study addresses the emerging challenge of nurse quit quitting. Findings can inform leadership, education and policy development globally, particularly in healthcare settings facing workforce strain, moral distress and retention challenges. REPORTING METHOD: This article adheres to the PRISMA-ScR reporting guidelines. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct, or reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".