Investigation of the Relationship Between Alexithymia and Cyberchondria in Nursing Students
Bibliographic record
Abstract
Introduction: Alexithymia and cyberchondria are significant psychological issues that can negatively affect the mental health, academic performance, and professional lives of nursing students. Aim: This study aims to examine the relationship between alexithymia and cyberchondria among nursing students and to identify the factors influencing this relationship. Method: This descriptive and cross-sectional study was conducted with 415 nursing students studying at the Faculty of Health Sciences of a university located in eastern Türkiye between April and June 2024. Data was collected via the Descriptive Information Form, the Toronto Alexithymia Scale, and the Cyberchondria Severity Scale Short Form. For the analysis of the data, descriptive statistics (percentages, means, standard deviations), Pearson correlation analysis, and regression analysis were used. Results: A statistically significant relationship was found between students' gender, academic grade, frequency of searching for health-related information on the internet, and total cyberchondria score (p < 0.05). Additionally, a significant relationship was found between the students' total alexithymia scores and having experienced a traumatic event in the past (p < 0.05). A positive and significant correlation was found between alexithymia and cyberchondria (r = 0.365, p < 0.001). According to the regression analysis, an increase in cyberchondria scores explained 13.3% of the variance in alexithymia. Conclusion: Our findings revealed a significant relationship between the alexithymia and cyberchondria levels of nursing students. These findings suggest that students who struggle to identify and express their emotions may tend to engage in excessive and uncontrolled online information-seeking behavior to alleviate healthrelated anxieties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".