Mobile Virtual Learning Object for the Assessment of Acute Pain as a Learning Tool to Assess Acute Pain in Nursing: An Analysis of the Mental Workload
Bibliographic record
Abstract
BACKGROUND: The inclusion of new technologies in education has motivated the development of studies on mental workload. These technologies are now being used in the teaching and learning process. The analysis enables identification of factors intervening in this workload as well as planning of overload prevention for educational activities using these technologies. OBJECTIVE: To analyze the mental workload of an educational intervention with the Mobile Virtual Learning Object for the Assessment of Acute Pain in adults and newborns, according to the NASA Task Load Index criteria. METHODS: A methodological study with data collected from 5 nurses and 75 students, from November of 2013 to February of 2014. RESULTS: The highest students' and specialists' means were in the dimensions of "Mental demand" (57.20 ± 22.27; 51 ± 29.45) and "Performance" (58.47 ± 24.19; 73 ± 28.85). The specialists' mental workload index was higher (50.20 ± 7.28) when compared with students' (47.87 ± 16.85) on a scale from 0 to 100 (P=.557). CONCLUSIONS: The instrument allowed for the assessment of mental workload after an online educational intervention with a mobile learning virtual object. An excessive overload was not identified among participants. Assessing mental workload from the use of educational technologies at the end of a task is a key to their applicability, with the aim of providing a more effective, stimulating, and long-lasting experience of the learning process.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".