Approaches for defining and assessing nursing informatics competencies: a scoping review
Notice bibliographique
Résumé
ABSTRACT Objective: The objective of this scoping review was to examine and map the literature on defining and assessing nursing informatics competencies for nurses and nursing students. Introduction: Over the past three decades, nursing informatics competency research has evolved markedly within countries and nursing roles. It is important to examine the available literature on defining and assessing nursing informatics competencies to inform education, clinical practice, policy, and future research. Inclusion criteria: We considered literature that defined or assessed the concept of nursing informatics competency as a combination of knowledge, skills, and attitudes. This included nursing informatics competencies of nurses and nursing students in a variety of health care or academic settings. Methods: An extensive search was conducted in Ovid MEDLINE, CINAHL Plus with Full Text via EBSCO, Ovid Embase, Ovid PsycINFO, ProQuest ERIC, Health and Psychosocial Instruments, ProQuest Australian Education Index, ProQuest Education Databases, ProQuest Dissertations and Theses Global, OCLC PapersFirst, Scopus, Web of Science Core Collection, Wiley Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and the JBI Database of Systematic Reviews and Implementation Reports. The initial search was conducted in May 2017 and updated several times. Nursing informatics websites were searched for gray literature, including unpublished research and organizational documents. Additional papers were identified based on a search of reference lists of all the included papers. Neither language nor date restrictions were applied. Two reviewers assessed each of the included papers independently. Data extraction was undertaken using an extraction tool developed specifically for the scoping review objectives. Results: Fifty-two papers were included. Thirty-four papers identified nursing informatics competencies, grouped into four categories: i) nursing informatics competencies for students, entry-level nurses, or generalist nurses; ii) nursing informatics competencies for a specific nursing role; iii) recommendations for consensus on defining core nursing informatics competencies at the international level; and iv) forecasting future nursing informatics competencies as per evolving nursing roles. Eighteen papers reported on nursing informatics competency assessment tools. Results were discussed in a narrative format supported by tables. Conclusions: This review provided insights to the state of the science on defining and assessing nursing informatics competencies for nurses and nursing students. Several nursing informatics competency lists are available, and despite some variations in domains of nursing informatics competency and indicator statements, they mostly share common themes. This literature demonstrates a heightened awareness of the importance of nursing informatics competency; however, the availability of many lists may be challenging for frontline nursing staff, nursing educators, administrators, researchers, and students to assimilate. Further research is needed to reach a consensus on core domains of nursing informatics competency and associated indicators, preferably per nursing roles, with international involvement and consensus. Additionally, while many nursing informatics competency assessment tools exist, further research is needed to examine psychometric properties of some of these tools.
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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 | 0,080 | 0,227 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,010 |
| Bibliométrie | 0,075 | 0,051 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,013 | 0,017 |
| Science ouverte | 0,005 | 0,009 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».