Impact of increased digital use and internet gaming on nursing students' empathy: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 restrictions and quarantines had led to increased dependence and usage of digital devices for various human activities and internet gaming to the extent of risking vulnerable individuals to develop addiction towards it. Little is known on such risks among populations of nursing students and its impact on their empathy skills or trait. OBJECTIVE: Determining the impact of digital use and internet gaming on empathy of nursing students undergoing remote learning during closure of learning institutions nationwide. DESIGN: Cross-sectional online survey was conducted from October to December 2020. SETTINGS: Two established public institutions located in Malaysia. PARTICIPANTS: A total of 345 nursing students pursuing diploma and bachelor nursing programs. METHODS: Toronto Empathy Questionnaire (TEQ), Digital Addiction Scale (DAS) and Internet Gaming Disorder Scale-Short form (IGDS9-SF) were self-administered via Google Form™. Following principal component analysis of TEQ using IBM-SPSS™ (V-27), path analyses was performed using SmartPLS™ (V-3). RESULTS: Despite the increased time spent on digital devices (∆ 2.8 h/day) and internet gaming (∆ 1 h/week) before and during the pandemic, the proportion of high digital users (1.4 %) and gamers (20.9 %) were low; and sizable ≈75 % had higher-than-normal empathy. Digital-related emotions and overuse of them were associated with lower empathy (β = -0.111, -0.192; p values < 0.05) and higher callousness (β = 0.181, 0.131; p values < 0.05); internet gaming addiction predicted callousness (β = 0.265, p < 0.001) but digital dependence correlated with higher empathy (β = 0.172, p = 0.009). CONCLUSIONS: Digital and internet gaming addiction potentially impact empathy. The negative impact of digital dependence can be attenuated by "digital empathy" - an emerging phenomenon becoming increasingly vital in digital health and communication.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".