45 Helping the Helpers: Peer Critical Incident Stress Management for NICU Health Care Providers to Improve Resilience, Burnout and Patient Safety
Bibliographic record
Abstract
Abstract Introduction/Background Health care providers in neonatal intensive care units (NICU) experience critical or distressing events that can overwhelm their usual coping skills and lead to significant stress. Ineffective support for health care providers dealing with critical incidents can lead to poor unit resilience, staff burnout and compromised patient care behaviours. A formalized peer program and process to address critical workplace incidents and support care providers, “Critical Incident Stress Management (CISM)” is used in many first responder professions. While there is growing interest in implementing peer CISM teams in critical care units, there is a lack of research describing the impact of CISM in NICU. Objectives This study examined the effect of implementing a multidisciplinary NICU health care provider peer CISM team on resilience, burnout, and team/safety culture in a tertiary NICU. Design/Methods Multidisciplinary team members were peer selected and formally CISM trained. Change management strategies were employed to introduce CISM to the NICU. All health care providers were invited to complete an anonymous online or paper survey before and 1 year after NICU CISM team implementation. The survey contained validated measures of resilience, burnout, and team/safety culture that were analyzed pre and post intervention. Results The response rate pre-intervention was 66% (114/172 staff) and 32% post (60/186 staff). Stress recognition significantly improved as fewer staff reported being less effective at work when feeling stressed post incident (74% vs 61%, pre and post CISM respectively, p<0.05) (Table 1). Fewer staff reported feeling burned out from their work (41% vs 31%, p=0.4), trending towards improved resilience (Table 1). Communication in the NICU significantly improved as staff indicated debriefing methods met their needs (38% vs 57%, p<0.05) and felt comfortable speaking up about safety concerns (66% vs 78%) (Table 1). Post-intervention, despite feelings of increased workload indicated by a significant decrease in agreement that “NICU staff levels were sufficient for patient load” (54% vs 33%, p<0.001), a majority of staff reported a supportive environment in the NICU (59% vs 77%, p=0.08) (Table 1). Work culture significantly improved as staff felt rewarded and recognized for improving quality (13% vs 31%, p<0.05) (Table 1). Conclusion Implementation of a peer CISM team led to improved NICU care provider resilience, stress recognition, and team culture, all of which can mitigate the effects of increased patient load. Findings from this research and knowledge gained from the CISM implementation process should be shared with other health care environments.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".