Gender Influences in the Intersection of Acute Care Registered Nurses and Law Enforcement
Bibliographic record
Abstract
To give voice to the lived experiences of nurses and law enforcement officers who interact with one another in an acute care hospital setting, while gaining an understanding of individual perspectives and unique experiences, as well as how they interpret these experiences. This qualitative study used interpretative phenomenological analysis (IPA) to strive to meet the study objectives. There is a paucity of literature on the topic of nurse and law enforcement interaction in the hospital setting. Overwhelmingly, participants described a contentious dynamic between nurses and law enforcement officers in the hospital, wrought with argument, stress, and a feeling of coming from "different worlds." The influence of gender was apparent to the female-identified participants, and gender constructs and therefore gender role conflict were critical points of contention. In exploring how nurses and law enforcement officers think about and describe their experiences, nurses and hospital systems may develop a deeper understanding and appreciation of barriers to care for incarcerated patients and of the challenging experiences nurses face in caring for these patients. The nurses' expressed feelings of intimidation, stress, and impaired self-efficacy in this dynamic underscore the need for institutional support and prioritization of caring practices, and identification of the ways in which carceral practices impair care, as well as nurses' safety.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".