Evaluation of Nursing Central as an Information Tool, Part I: Student Learning
Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».