Interventions to reduce occupational stress and burn out within neonatal intensive care units: a systematic review
Bibliographic record
Abstract
Occupational stress is an emerging problem among physician and nurses, and those working in intensive care settings are particularly exposed to the risk of developing burnout. To verify what types of interventions to manage occupational stress and burn out within neonatal intensive care units (NICUs) have been introduced so far and to verify their efficacy among caregivers. PsycINFO (PsycINFO 1967-July week 3 2019), Embase (Embase 1996-2019 week 29) e Medline (Ovid MEDLINE(R) without revisions 1996-July week 2 2019) were systematically searched combining MeSH and free text terms for "burn out" AND "healthcare provider" AND "NICU". Inclusion criteria were interventions directed to healthcare providers settled in NICUs. Only English language papers were included. Six articles were included in the final analysis. All the studies reported an overall efficacy of the interventions in reducing work-related stress, both when individual focused and organisation directed. The analysis revealed low quality of the studies and high heterogeneity in terms of study design, included populations, interventions and their evaluation assessment. There is currently very limited evidence regarding the management of occupational stress and burn out within NICUs. The quality of available studies was suboptimal. The peculiarities of the NICUs should be considered when developing strategies for occupational stress management. Training self-awareness of workers regarding their reactions to the NICU environment, also from the pre-employment stage, could be an additional approach to prevent and manage stress.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".