Smartphone Addiction, Personality Factors, Emotional Regulation and Mental Health: Gender Based Studies
Bibliographic record
Abstract
Introduction: The present study intends to measure gender differences in smartphone addiction, emotional regulation, mental health and personality variables. Earlier studies have focused on gender differences in variables under study. Aim: It was an attempt to revalidate the findings for fulfillment of the research objectives. Sample: The sample was drawn following some predetermined inclusion and exclusion, a sample of 100 individuals ageing between 13 to 19 years were selected. Half of them were male and half of them were females. Method: Sample was administered with 1) Smartphone addiction test by Kwon et al (2013), 2) Mental Health Inventory Veit and Ware (1983), 3) Emotional Regulation Questionnaire by James J. Gross and Oliver P (2003), 4) Big Five Inventory (Personality) by Goldberg, (1993). Along with the above measures General Health Questionnaire was also administered for screening purpose. For GHQ a cut off 4 was selected below which individuals were allowed for participation in investigation. Result: Results indicated gender differences in variables under study. Females have been found to have higher level of addiction than male. Male were found to have more distress and low in psychological well-being. Male also found to have greater emotional reappraisal and lesser emotional suppression. Conclusion: In case of personality variables males were found to be more open, conscientious and agreeable while females were found to be more prone to neuroticism.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".