Understanding the Lived Experiences of Nurses Resuscitating Children in Community Hospital Emergency Departments
Bibliographic record
Abstract
Emergency department (ED) nurses exposed to pediatric resuscitations are at a high risk of developing posttraumatic stress (Adriaenssens et al., 2012; Lavoie et al., 2016). This may be especially true in community hospital EDs where nurses have less exposure to, knowledge about, and resources for managing these events (Gangadharan et al., 2018; Gilleland et al., 2014; Goldman et al., 2018). Interventions to proactively prevent nurse trauma in these contexts remain uninvestigated. To inform such interventions, this study aimed to understand the largely unknown lived experiences of these nurses. In-depth, semi-structured interviews were conducted with four registered nurses who experienced at least one pediatric resuscitation while working in a community hospital ED in Ontario. Data analyzed using Smith et al.’s (2009) interpretive phenomenological analysis revealed three superordinate themes (i.e., “Conceptualizing Pediatric Resuscitations,” “Seeing What I See,” and “Making Sense of What I Saw”) and nine corresponding subthemes. This study provides insight into the infrequent but profound experiences of nurses resuscitating children in community hospital EDs. Participants, who conceptualized these events as unnatural, emotional, and chaotic, were comforted by those who understood their experiences and distressed by those who could not see what they saw. To reconcile what they saw, the nurses reflected and ruminated 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. The findings of this study have implications for nursing practice, education, leadership, and research that may enhance nurse coping following these events.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".