What is known about paediatric nurse burnout: a scoping review
Bibliographic record
Abstract
Burnout in healthcare providers has impacts at the level of the individual provider, patient, and organization. While there is a substantial body of literature on burnout in healthcare providers, burnout in pediatric nurses has received less attention. This subpopulation may be unique from adult care nurses because of the specialized nature of providing care to children who are typically seen as a vulnerable population, the high potential for empathetic engagement, and the inherent complexities in the relationships with families. Thus, the aim of this scoping review was to investigate, among pediatric nurses, (i) the prevalence and/or degree of burnout, (ii) the factors related to burnout, (iii) the outcomes of burnout, and (iv) the interventions that have been applied to prevent and/or mitigate burnout. This scoping review was performed according to the PRISMA Guidelines Scoping Review Extension. CINAHL, EMBASE, MEDLINE, PsycINFO, ASSIA, and The Cochrane Library were searched on 3 November 2018 to identify relevant quantitative, qualitative, and mixed-method studies on pediatric nurse burnout. Our search identified 78 studies for inclusion in the analysis. Across the included studies, burnout was prevalent in pediatric nurses. A number of factors were identified as impacting burnout including nurse demographics, work environment, and work attitudes. Similarly, a number of outcomes of burnout were identified including nurse retention, nurse well-being, patient safety, and patient-family satisfaction. Unfortunately, there was little evidence of effective interventions to address pediatric nurse burnout. Given the prevalence and impact of burnout on a variety of important outcomes, it is imperative that nursing schools, nursing management, healthcare organizations, and nursing professional associations work to develop and test the interventions to address key attitudinal and environmental factors that are most relevant to pediatric nurses.
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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 | 0.018 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".