‘They all stay with me’—An interpretive phenomenological analysis on nurses’ experiences resuscitating children in community hospital emergency departments
Bibliographic record
Abstract
AIM: To understand the lived experiences of nurses resuscitating children in community hospital emergency departments. BACKGROUND: Emergency department nurses exposed to paediatric resuscitations are at a high risk of developing post-traumatic stress. This may be especially true in community hospital emergency departments, where nurses have less exposure to, knowledge about, and resources for managing these events. Interventions to proactively prevent nurse trauma in these contexts remain largely uninvestigated. To inform such interventions, a detailed understanding of the largely unknown lived experiences of these nurses is necessary. DESIGN AND METHODS: In-depth, semi-structured interviews were conducted with four registered nurses that had experienced at least one paediatric resuscitation while working in a community hospital emergency department in Ontario, Canada. Data were analysed using interpretive phenomenological analysis. Reporting follows the COREQ checklist. RESULTS: Analysis revealed three superordinate themes (i.e. 'Conceptualising Paediatric Resuscitations', 'Seeing What I See', and 'Making Sense of What I Saw') and nine corresponding subthemes. CONCLUSION: This study provides insight into the infrequent, but profound experiences of nurses resuscitating children in community hospital emergency departments. Nurses, who conceptualise these events as unnatural, emotional, and chaotic, are comforted by those who understand their experiences and are distressed by those who cannot see what they see. To reconcile what they have seen, nurses may reflect and ruminate on the event, ultimately restructuring their experiences of themselves, others, and the world to make room for a new reality where the safety of childhood is not certain. RELEVANCE TO CLINICAL PRACTICE: Our findings contribute to pragmatic recommendations for interventions to proactively prevent nurse distress in these contexts, including psychoeducation, psychological support and in-situ simulation activities. Nursing leaders should consider staff that have resuscitated children as valuable sources for information on how to improve practice settings.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".