"What Were You Doing To Get Shot?" Exploring The Perceptions And Experiences Of Critical CareTrauma Nurses On Gun Violence And Caring For Persons With Gun Violence Injuries
Bibliographic record
Abstract
Rising gun violence (GV) means critical care trauma nurses (CCTNs) are caring for those most impacted, navigating complex feelings, family needs and very sick patients. I asked, “What can I do from the bedside?” Grounded in the socio-ecological model and complexity theory, a qualitative exploration of CCTNs’ perceptions and lived experiences began. Community-based professionals’ (CBPs) were consulted and CCTNs interviewed. Thematic analysis allowed four themes to emerge: one from the CBP, highlighting the need for persons with GV injuries to feel safe; the remaining themes tell nurses’ understudied stories, attempts to makes sense of the senseless and the toll of bearing witness. Overarchingly, trauma emerged for those in the bed and those caring for them from the bedside, reminding us that We are all humans – in trauma. Shared experiences of trauma provide the opportunity to engage in activities that improve the lives of CCTNs and those living with GV.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".