Study on the relationship between alexithymia and problematic mobile phone usage among high vocational college students
Bibliographic record
Abstract
Objective Based on the description of epidemic characteristics of problematic mobile phone usage(PMPU)among high vocational college students,we analyzed the relationship between PMPU and alexithymia in order to provide evidence for the intervention of PMPU. Methods Students from three high vocational colleges in Anhui Province were recruited by cluster sampling and their PMPU and alexithymia were respectively evaluated by the Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use(SQAPMPU)and Toronto Alexithymia Scale(TAS-20). Results The prevalence of moderate and high dependence of PMPU was 49.3% and 26.4%,respectively.The prevalence of moderate and high dependence of PMPU was higher among girls(79.1%)than boys(67.8%,P0.001).The TAS scores and factor scores of alexithymia in the high dependence group of PMPU were higher than those in the low and moderate dependence group(P0.001).A positive correlation existed between the PMPU and alexithymia with respect to both their total scores and factor scores.When the gender,age and other variables were controlled,the group without alexithymia had lower risk for moderate and high PMPU than that with alexithymia(OR=0.49,95%CI=0.35~0.70;OR=0.18,95%CI=0.13~0.25). Conclusions The PMPU is common among high vocational college students in Anhui Province.The students with alexithymia are the key population for the intervention of PMPU and should be paid more concern.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".