The Effect of Nurses' Cyberloafing Levels on Their Perceptions of Individualized Care
Bibliographic record
Abstract
While cyberloafing behaviors can have positive effects, such as introducing variety into daily routines and alleviating workplace stress, excessive engagement in these activities can become problematic, leading to time wastage and decreased efficiency. In nursing practice, individualized care is essential for ensuring both the quality of patient care and patient safety. This study aims to identify the cyberloafing behaviors of nurses and examine their relationship with individualized care behaviors. The research is a cross-sectional and descriptive study. The Descriptive Information Form, the Cyber-Loafing Scale, and the Individualized Care Scale-Nurse Version were used for data collection. Nurses showed a moderate level of cyberloafing behavior with a mean total score of 80.99 ± 22.44. Nurses also showed a moderate perception of care behavior, with an average total score of 3.20 ± 0.81 on the Individualized Care Scale. A positive, low-level, significant relationship was found between the total score of the Cyber-Loafing Scale and the Individualized Care Scale-A ( r = 0.199, P = .01). The study revealed that nurses' perception of individualized care improved as their level of cyberloafing increased. This may be because cyberloafing behaviors serve as a constructive way for nurses to escape from routine practices in the hospital environment and reduce anxiety.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".