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The status of training and education in information and computer technology of Australian nurses: a national survey

2008· article· en· W2099142166 on OpenAlex
Robert Eley, Tony Fallon, Jeffrey Soar, Elizabeth Buikstra, Desley Hegney

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Clinical Nursing · 2008
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersUniversity of Southern QueenslandAustralian Government
KeywordsComputer literacyInformation technologyWork (physics)Computer technologyMedical educationNursingComputer trainingQuarter (Canadian coin)Training (meteorology)MedicinePsychologyComputer scienceMultimedia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.559
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it