ABSTRACT 949
Bibliographic record
Abstract
Background and aims: Of approximately 1100 admissions to our PICU per year, on average 25 children die. Despite low numbers, when discussing workplace stressors, ICU nurses identified these experiences as the most distressing. Moreover such distress is cumulative; thus, the issue becomes significant despite relatively low numbers of deaths experienced. Staff rarely get an arranged opportunity to address the emotional impact of the death before the end of their shift as defusings do not regularly occur because of an inability to lead them, a perceived lack of time or an understanding of their importance by the staff present. Aims: To initiate a quality improvement project to provide staff with defusings following the death of a child in the PICU. Methods: Defusing training was provided for key staff members, i.e., Charge Nurses, physicians, Spiritual Care providers, Social Workers, and end of life committee members. The training provided these facilitators with essential information and defusing skills, equipping them with competencies to lead defusings after patient deaths.This project did not require IRB approval. Results: The education sessions were evaluated with staff feedback; after a 6-month trial period, we will also evaluate the defusing program by surveying the facilitators and participants. Conclusions: Defusing sessions can be a valuable means of supporting PICU staff in managing their grief while caring for children at the end of their lives. Defusings are an example of an intervention that our unit is providing as part of a comprehensive approach in supporting its staff. The use of defusings following other critical situations will be explored.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.848 | 0.734 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".