Computer-based nursing education: An integrative review of empirical studies
Bibliographic record
Abstract
The goal of this study is to explore the ways in which Computer-Based Nursing Learning (CBNL) has been studied and the findings that have been made with regard to its use in undergraduate nursing education. We undertook an integrative review by selecting papers published in English between 2007 and 2010. We included in the review empirical studies comparing CBNL with other training strategies for clinical skills education in the context of undergraduate nursing education. We carried out an electronic search in which specific keywords were used, and a total of 467 citations were found. Nine of these studies met the inclusion criteria. A list of criteria for evaluating the quality of the empirical studies identified was also used. With regard to the impact of CBNL on skill performance and cognitive recall, the results were positive since most studies reported higher skill and knowledge scores using CBNL. Only two studies tested skill or cognitive retention. Seven studies reported high levels of students' satisfaction with CBNL. However, the authors identified some problems related to technical issues in four studies. Finally, we described and criticized the experiences, since important weaknesses in the experimental designs were detected. We also provided some recommendations for better practices in the research methods.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".