The status of training and education in information and computer technology of Australian nurses: a national survey
Notice bibliographique
Résumé
AIMS AND OBJECTIVES: A study was undertaken of the current knowledge and future training requirements of nurses in information and computer technology to inform policy to meet national goals for health. BACKGROUND: The role of the modern clinical nurse is intertwined with information and computer technology and adoption of such technology forms an important component of national strategies in health. The majority of nurses are expected to use information and computer technology during their work; however, the full extent of their knowledge and experience is unclear. DESIGN: Self-administered postal survey. METHODS: A 78-item questionnaire was distributed to 10,000 Australian Nursing Federation members to identify the nurses' use of information and computer technology. Eighteen items related to nurses' training and education in information and computer technology. RESULTS: Response rate was 44%. Computers were used by 86.3% of respondents as part of their work-related activities. Between 4-17% of nurses had received training in each of 11 generic computer skills and software applications during their preregistration/pre-enrolment and between 12-30% as continuing professional education. Nurses who had received training believed that it was adequate to meet the needs of their job and was given at an appropriate time. Almost half of the respondents indicated that they required more training to better meet the information and computer technology requirements of their jobs and a quarter believed that their level of computer literacy was restricting their career development. Nurses considered that the vast majority of employers did not encourage information and computer technology training and, for those for whom training was available, workload was the major barrier to uptake. Nurses favoured introduction of a national competency standard in information and computer technology. CONCLUSIONS: For the considerable benefits of information and computer technology to be incorporated fully into the health system, employers must pay more attention to the training and education of nurses who are the largest users of that technology. RELEVANCE TO CLINICAL PRACTICE: Knowledge of the training and education needs of clinical nurses with respect to information and computer technology will provide a platform for the development of appropriate policies by government and by employers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».