Accessing Best Practice Resources Using Mobile Technology in an Undergraduate Nursing Program
Bibliographic record
Abstract
Mobile technology presents new opportunities for nursing education and ultimately the provision of nursing care. The aim of this study was to explore the utility of mobile technology in undergraduate nursing education. In this evaluation study, undergraduate nursing students were provided with iPod Touch devices containing best practice guidelines. Computer self-efficacy was assessed, and the Theory of Planned Behavior was used to identify potential predictors of the use of mobile technology. Questionnaires were completed at baseline (n = 33) and postimplementation (n = 23). Feedback on feasibility issues was recorded throughout the study period. Students generally found the devices useful, and few technical problems were identified; however, lack of skill in using the devices and lack of support from staff in the clinical setting were commonly identified issues. Self-efficacy scores were high throughout the study. Attitudes, perceptions of the desirability of use, perceived personal control over use, and intentions of using the device were lower postimplementation than at baseline. Attitude toward the technology predicted intention to use the device after graduation. Mobile technology may promote evidence-informed practice; however, supporting students' acquisition of related skills may optimize use. Successful integration of mobile technology into practice requires attention to factors that affect student attitudes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".