Perception of Artificial Intelligence Technology and Its Relation to Problem-Solving Abilities among Staff Nurses
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".