The impact of mobile technologies on new graduate nurses’ perceived self-efficacy and clinical decision making: A report from a longitudinal study in Western Canada
Bibliographic record
Abstract
Healthcare environments require practitioners to competently and independently collect pertinent data, select appropriate key resources, prioritize information, solve problems, and make sound clinical decisions. The steady increase of health-related information implies a need for useful, practical Information and Communication Technology (ICT) tools that easily provide nurses’ access to accurate evidence-based information. The purpose of this study was to explore the impact of using mobile technologies at the point of care on new graduates’ perceived clinical decision making ability and associated level of self-efficacy over time. A longitudinal quasi-experimental pre-test/post-test design was used. A trend in the findings of this small study suggests that over time, using mobile technologies at the point of care did not enhance the participants’ perceived clinical decision making ability or self-efficacy in clinical decision making. Notwithstanding, the use of mobile technologies in the practice setting is wide spread. It, however, may be that the transition from student to graduate nurse is a significant enough event that seriously limits the useful influence of mobile devices and their associated applications on clinical decision making ability and self-efficacy.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".