A NURSING HANDBOOK FOR NEW GRADS: The journey to excellence continues! [McKenzie RN, Patricia N] on Amazon.com. *FREE* shipping on qualifying offers. A NURSING HANDBOOK FOR NEW GRADS: The journey to excellence continues!

Nurses across Australia and New Zealand—We Need Your Input We are inviting nurses to participate in a survey exploring how aseptic technique is understood and…

TITLE: SCHOOL NURSE QUALIFICATIONS: 1. Valid New Jersey School Nurse Certificate2. Demonstrated expertise in school health nursingpractice and emergency procedures3. Knowledge of child growth and development,community and family dynamics, current healthissues, and wellness education4. Knowledge of community health and socialservices resources and ability to communicateeffectively with nonschool health professionalsand social service agencies5. Required criminal history background check andpro...

La Comisión de Salud recibió hoy ponencias sobre el Proyecto del Senado 757, que busca armonizar la Ley de Farmacia con la Ley de Enfermería para que los nurse practitioners puedan ver sus recetas despachadas; el secretario de Salud pidió enmiendas técnicas y la comisión exigió análisis curricular y límites claros.

Background: Clinical AI decision support is being introduced into #nursing practice; however, existing large language models (LLMs) demonstrate only moderate accuracy on complex clinical tasks, raising questions about the level of accuracy required for safe clinical use across varying levels of clinician experience and task complexity. Objective: The aim of the study is to develop an empirically calibrated simulation model of human-AI reliance and error in #nursing decision-making and estimate the AI accuracy required to achieve specified error rate targets. Methods: A linear reliance model with coefficients for AI accuracy (A), clinician experience (E), and task complexity (C) was calibrated using weighted least squares against 9 empirical data points from 3 independent randomized experiments on AI-assisted decision-making (N=3502). Predicted error was computed as reliance×(1−) across a 27-cell factorial design. Study-level bootstrap (2000 iterations) quantified calibration uncertainty. To contextualize the simulation’s operating range, the accuracy of contemporary general-purpose LLMs on complex clinical tasks was drawn from published benchmarks. Results: Calibration placed β at 0.201 (bootstrap 95% CI 0.023‐0.234; (β>0)>.99). For the novice×high-complexity combination, the minimum AI accuracy values required to achieve error rates