Nurses’ Use of Mobile Devices to Access Information in Health Care Environments in Australia: A Survey of Undergraduate Students
Bibliographic record
Abstract
BACKGROUND: The growth of digital technology has created challenges for safe and appropriate use of mobile or portable devices during work-integrated learning (WIL) in health care environments. Personal and professional use of technology has outpaced the development of policy or codes of practice for guiding its use at the workplace. There is a perceived risk that portable devices may distract from provision of patient or client care if used by health professionals or students during employment or WIL. OBJECTIVE: This study aimed to identify differences in behavior of undergraduate nurses in accessing information, using a portable or mobile device, when undertaking WIL compared to other non-work situations. METHODS: A validated online survey was administered to students while on placement in a range of health care settings in two Australian states. RESULTS: There were 84 respondents, with 56% (n=47) reporting access to a mobile or portable device. Differences in use of a mobile device away from, compared with during WIL, were observed for non-work related activities such as messaging (P<.001), social networking (P<.001), shopping on the Internet (P=.01), conducting personal business online (P=.01), and checking or sending non-work related texts or emails to co-workers (P=.04). Study-related activities were conducted more regularly away from the workplace and included accessing University sites for information (P=.03) and checking or sending study-related text messages or emails to friends or co-workers (P=.01). Students continued to access nursing, medical, professional development, and study-related information away from the workplace. CONCLUSIONS: Undergraduate nurses limit their access to non-work or non-patient centered information while undertaking WIL. Work-related mobile learning is being undertaken, in situ, by the next generation of nurses who expect easy access to mobile or portable devices at the workplace, to ensure safe and competent care is delivered to their patients.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
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".