Mobile Phone Addiction Mediates the Relationship Between Alexithymia and Learning Burnout in Chinese Medical Students: A Structural Equation Model Analysis
Bibliographic record
Abstract
Background: Learning burnout is a passive mental state among students. It is a common phenomenon that can cause many bad outcomes in Chinese medical students, such as mental disorders and suicide, and its causes are complex. Purpose: To analyze the relationship between alexithymia and learning burnout, as well as the mediating effect of mobile phone addiction, and provide clues for future interventions to deal with learning burnout among Chinese medical students. Methods: In this cross-sectional study, convenience cluster sampling was used to produce a sample of 1200 medical universities in Chongqing, China. The Toronto Alexithymia Scale (TAS-20), Mobile Phone Addiction Tendency Scale (MPATS), and Learning Burnout Questionnaire (LBQ) were used to examine participants. Hierarchical regression was used to analyze the effect of alexithymia and mobile phone addiction on learning burnout. A structural equation model (SEM) with maximum likelihood was used to evaluate the mediating effect of mobile phone addiction on the relationship between alexithymia and learning burnout. The bootstrap method was used to confirm the significance of this mediating effect. Results: The final sample size was 1062, with a valid response rate of 88.5%. The prevalence of learning burnout among Chinese medical students was 39.6%. Results of hierarchical regression revealed that alexithymia (ΔR 2 =0.198, P< 0.01) and mobile phone addiction (ΔR 2 =0.021, P< 0.01) were independent factors of learning burnout; the SEM revealed that the mediating effect of mobile phone addiction between alexithymia and learning burnout accounted for 25.16% of the total effect of alexithymia on learning burnout; the bootstrap method revealed that the bounds of the CI did not contain 0, confirming the significance of this mediating effect. Conclusion: Of the medical students, 39.6% had learning burnout. Alexithymia can positively predict learning burnout, and this relationship is partially mediated by mobile phone addiction. Keywords: learning burnout, alexithymia, mobile phone addiction, medical students, mediating effect, interventions
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".