Evaluation of Nursing Central as an Information Tool, Part I: Student Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order that graduates have the ability to access, manage, and apply up-to-date information (Scollin, Healey-Walsh, Kafel, Mehta, & Callahan, 2007) and make evidence-informed decisions, nurse educators must integrate informatics and information management tools within the nursing curriculum (Canadian Association of Schools of Nursing, 2012; Hebda & Calderone, 2010; National League for Nursing, 2008). Nurse experts have recommended that pre-licensure nurses use smart information technologies to enable them to provide informed, safe, and competent patient care (TIGER, 2009). Smart technologies, such as handheld devices, improve access to clinical information and drug dosage calculations (Farrell & Rose, 2008; White et al., 2005). Such devices, including smartphones, iPods, and tablets, can house current, reliable, digital resources and textbooks. Current studies suggest that, with handheld devices, nursing students search for information more frequently, make fewer medication errors (Koeniger-Donohue, 2008), and report more positive attitudes and lower stress when acquiring clinical information (Jamieson et al., 2009). Students in one small, local study highly recommended adoption of Nursing Central [TM] (NC) as an information support software system for clinical learning (Jamieson et al., 2009). However, more knowledge about student perspectives on the usability and usefulness of specific information-support software and technologies is required before adoption of this learning resource. METHOD This pilot study evaluated the perspectives of nursing students who used NC in a clinical practice rotation. NC, a software program developed by Unbound(r) Medicine (Charlottesville, VA) specifically for nurses, enables the download and searching of electronic quick reference guides (Taber's Medical Dictionary, Davis's Drug Guide for Nurses, Davis's Comprehensive Handbook of Laboratory and Diagnostic Tests, Davis's Diseases and Disorders, and Handbook of Nursing Diagnosis) to a handheld device. NC also enables Medline search capability and download of journal Table of Contents alerts. For a trial period, Unbound Medicine donated NC software licenses for interested students and waived the annual individual software fee of US$159. Fourth-year students were oriented to NC in the classroom while third-year students received orientation in the clinical setting before use during a clinical rotation. While the third-year students completed their clinical rotation with a clinical instructor who also used NC, the fourth-year students used NC independently in their final preceptored placement. One hundred fifty students (third-year, 70; fourth year, 80) from a four-year baccalaureate nursing program were invited to participate in the pilot study. Students were eligible if they had an appropriate handheld device or smartphone and, for third-year students, if their clinical instructor also used NC. From this population, 80 nursing students requested an NC licence from Unbound Medicine, and 65 students downloaded software programs. Recruitment for the study began after courses were completed, course grades were received, and ethics approval was provided by the university ethics committee. The study explanation and online survey link (Askitonline.com) were emailed to participants; all study data were anonymous. The survey consisted of 25 Likert items with scores ranging from 1 (do not agree) to 5 (strongly agree), with one item rating of overall helpfulness and three textboxes designed to measure NC user-friendliness, information support, and resource quality for clinical learning. RESULTS A total of 31 students completed the online survey (third year, n = 16; fourth-year, n = 15). Their average age was 25.86 years, with ages ranging from 20 to 43 years. Quantitative Findings The rating of overall helpfulness of NC as a resource during the clinical rotation was high (M = 4. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it