Empathy and Mobile Phone Dependence in Nursing: A Cross-Sectional Study in a Public Hospital of the Island of Crete, Greece
Bibliographic record
Abstract
This study examined the relationship between empathy and mobile phone dependence levels of the nursing staff in a public hospital in the island of Crete, using a cross-sectional study design. Data from 109 staff nurses and healthcare assistants (HCAs) were collected via the Greek version of the Mobile Phone Dependence Questionnaire (MPDQ) and the Toronto Empathy Questionnaire (TEQ). Multiple linear regression was used to determine the correlation between empathy and mobile phone dependency. The total mean score for TEQ was 33.9 (±5.7). Accordingly, the total mean score for MPDQ was 22.9 (±6.1). High mobile phone dependence was found in 4.7% of the participants. A statistically significant difference was found between HCAs and staff nurses, with HCAs presenting a higher mean empathy levels (TEQ) (36.5 vs. 32.6) and lower dependence levels (MPDQ) (18.9 vs. 24.5) than staff nurses. A significant correlation between empathy and dependence was found between dependence and the altruism empathy subscale, with higher dependence being correlated with lower altruism. The participants' levels of empathy do not seem to be affected by mobile phone dependence. However, empathy appears to be strongly determined by increased age and professional status. Nurses' dependence on mobile phones is a complex phenomenon that requires attention. Educational programs on empathy and information on the proper use of mobile phones by the nursing staff should be provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".