Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to give voice to the lived experiences of nurses and law enforcement officers (LEOs) who interact with one another in acute hospital settings and to interpret and understand their unique perspectives and experiences. METHODS: This qualitative study employed interpretative phenomenological analysis in the interviews of registered nurses and LEOs. The analysis and discussion was underpinned by biopolitical theories of power and control, including Georgio Agamben, Michel Foucault, and Erving Goffman. RESULTS: There is a paucity of literature on nurse and law enforcement interactions in the hospital setting. Nurses and law enforcement exerted power and authority through several means. Overwhelmingly, participants described a contentious dynamic between nurses and LEOs in the hospital, wrought with argument, stress, and a feeling of coming from "different worlds." CONCLUSION: The results provide alarming examples of deformed caring practices and assert the necessity for continued unearthing and discussion of how nurses can, and should, navigate law enforcement interaction. The tangible interference of care is of particular importance and consideration for nurses. Inequity in care and unfavorable outcomes for already marginalized and vulnerable populations are of grave concern. Additional research is needed on the specific ways this struggle for power between institutions and their political actors impairs caring practices and the emotional and psychological sequelae of these interactions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".