TIGER-based measurement of nursing informatics competencies: The development and implementation of an online tool for self-assessment
Notice bibliographique
Résumé
Background/Objective: The aim of this research was to develop a reliable, valid instrument for self-assessment of perceived nursing informatics (NI) competencies. This article describes the development and validity assessment of the instrument. Informatics competencies are deemed a necessity in today’s technologically-rich healthcare delivery system. Work to identify essential informatics skills commenced shortly after the introduction of information technology into healthcare. In subsequent years, professional organizations and individual experts have established NI competencies needed at various levels of nursing practice, from entry level through advanced practice. The Technology Informatics Guiding Educational Reform (TIGER) Initiative represents one such effort. The TIGER Initiative emerged in 2006 as a grassroots effort dedicated to the preparation of a clinical workforce capable of using information technology and informatics to improve the delivery of healthcare. TIGER quickly organized into several different collaborative groups, including one that identified a set of recommended informatics competencies for nurses in 2009. The TIGER effort listed NI competencies in three areas: basic computer skills, information literacy, and clinical information management but did not operationalize these competencies into an instrument that could be used for assessment purposes. Methods: Three rounds of reviews were conducted. In the first review, the researchers examined TIGER competencies, removing duplicative terms and combining items with similar content. The second and third rounds of reviews were each done by two separate sets of three experts in nursing informatics. During the second round, the list from round one was examined for items to retain or add. Resulting items were reworded to reflect measurable behaviors and then subjected to a third round of reviews to establish content validity, using the content validity index (CVI) methodology. Results: CVIs demonstrated moderate validity for the instrument, and items not deemed relevant to the objective of the instrument were deleted, reducing the number of the items on the instrument. The instrument was piloted by posting the invitation on the online discussion forum of a nursing informatics organization. An additional invitation was extended to a group completing a weekend-long NI course. There were 184 respondents. Most respondents ranked themselves as expert on the majority of items, although a lesser degree of confidence was seen with items related to information literacy Conclusions: TIGER competencies provided a useful foundation for the creation of a feasible online instrument for self-assessment of levels of competency. Fewer respondents identified themselves as expert in information literacy competencies. The instrument developed for this research project could be useful in planning educational opportunities in NI.
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 | 0,061 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».