The status of training and education in information and computer technology of Australian nurses: a national survey
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".