The Relationship of Smartphone Addiction with Depression, Obsession-Compulsion, Impulsivity, Alexithymia Among Medical Faculty Students
Bibliographic record
Abstract
Objective: The purpose of current study is to examine the relationship of smartphone addiction (SA) with depression, obsessive-compulsive symptoms (OCS), impulsivity and alexithymia among medical faculty students.Method: A total of 345 medical faculty students who reported smartphone use enrolled in study. Thirty five students who did not fully fill out the questionnaires excluded. Participants completed sociodemographic information form, Smartphone Addiction Scale (SAS), 20-item Toronto Alexithymia Scale (TAS-20), Short Form of Barratt Impulsiveness Scale (BIS-11-SF), and depression and obsessive-compulsive subscales of Symptom Checklist-90 Revised (SCL-90-R) online.Results: As 57.1 % of participants (n=177) are female, 42.9 % of them are male (n=133). Mean of age was 22.40 (SD=±2.40). SA has significantly positive relationship with depression (r=0.351), OCS (r=0.291), difficulty in identifying feelings (r=0.453), impulsivity (r=0.451), difficulty in describing feelings (r=0.860) and alexithymia total (r=0.688). Moreover results of three-stepped Hierarchical regression analysis revealed that depression significantly predicted SA (p<0.001). In the second step, both depression (p<0.05) and impulsivity (p<0.001) significantly predicted SA. In the last step of regression equation, model comprising of impulsivity (p<0.001), difficulty in identifying feelings (p<0.05) and difficulty in describing feelings (p<0.001) and externally oriented thinking (p<0.05) significantly predicted SA.Conclusion: Prominent finding of current study is that although impulsivity and alexithymia have been known close to behavioral addictions, even when depression and OCS were controlled, still they were significant and unique predictors of SA. No matter time and place do smartphones serve availability to features like online gaming and connecting social media beside speaking on the phone and messaging. Therefore, availability of and accessibility to smartphone may be a risk factor for addiction. When merging of properties of smartphones and personality features like impulsivity and alexithymia may yield SA in young adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".