Perception of Artificial Intelligence Technology and Its Relation to Problem-Solving Abilities among Staff Nurses
Why this work is in the frame
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Bibliographic record
Abstract
Background: The rapid integration of artificial intelligence into various sectors including health care has heightened the need for understanding its impact on nurses cognitive and problem-solving abilities. Purpose: To assess staff nurses perception of artificial intelligence technology and its relation to nurses' problem-solving abilities. Design: descriptive Correlational research design was used. Setting: Conducted at the critical care units and general departments at Menoufia University Hospitals at Shebin Elkom. Sample: A convenient sample technique of 306 staff nurses. Instruments: Artificial Intelligence Technology and Problem Solving Abilities Questionnaires. Results: The minority (15.0%) of the studied nurses had high perception level of total artificial intelligence and nearly one third (29.1%) of them had moderate perception level of total artificial intelligence while nearly two third (55.9 %) of them had low perception level of total artificial intelligence. Also, less than one third (30.1%) of the studied nurses had high level of problem-solving abilities, less than one quarter (22.2%) of them had moderate level of problem-solving abilities. while, less than half (47.7%) of them had low level of problem-solving abilities. Conclusion: there was low statistically significance positive correlation between studied nurses' artificial intelligence and problem-solving abilities. Recommendation: Hospital administration conduct workshop and training programs to increase nurses’ knowledge about the benefits, challenges, and problems concerning implementation of artificial intelligence in health care settings.
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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 | 0.001 |
| 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 | 0.000 | 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 it