Association between fatigue and Internet addiction in female hospital nurses
Bibliographic record
Abstract
AIMS: To report a study conducted to examine the association between fatigue and Internet addiction among female hospital nurses. BACKGROUND: The Internet provides unprecedented convenience for social interaction and information retrieval. Although excessive Internet use has been demonstrated to correlate with fatigue in adolescents, no studies have examined whether it is associated with fatigue in nurses. DESIGN: Cross-sectional survey. METHODS: The study was conducted in August 2010. Female Registered Nurses working in a regional teaching hospital in southern Taiwan were asked to complete a paper-based questionnaire. The questionnaire included questions on demographics, the Chen Internet Addiction Scale and the Chalder Fatigue Scale. Multiple linear regression analysis was performed using Chalder fatigue scale as the dependent variable. RESULTS: Of the 564 (79% response) valid questionnaires returned, 6 and 10% of the participants were classified as diagnostic cases and possible cases of Internet addiction, respectively. Fatigue levels, adjusting for other potential confounders including work unit, shift work, regular self-medication, and self-perceived health status, was significantly associated with both possible cases of Internet addiction and diagnostic cases of Internet addiction. CONCLUSION: This study is the first in reporting a statistically significant association between fatigue levels and Internet addiction in female hospital nurses. RELEVANCE TO CLINICAL PRACTICE: Nurses should pay attention to their Internet activity and whether it adds to their fatigue levels. Addictive behaviour should promptly be dealt with to ensure that the best care is provided to patients.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".