Exploring the Differentiation of Self-Concepts in the Physical and Virtual Worlds Using Euclidean Distance Analysis and Its Relationship With Digitalization and Mental Health Among Young People: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Increasing observation and evidence suggest that the process of digitalization could have profound impact to the development of human mind and self, with potential mental health consequences. Self-differentiation is important in human identity and self-concept formation, which is believed to be involved in the process of digitalization. OBJECTIVE: This study aimed to investigate the relationship between digitalization and personal attributes in the actual selves in the physical and virtual worlds. METHODS: A community cohort of 397 participants aged 15 to 24 years old was recruited consecutively over about 3 months. Assessment was conducted upon the indicators of digitalization (smartphone use time, leisure online time, and age of first smartphone ownership), smartphone addiction, 14 selected personal attributes in the actual selves in the physical and virtual worlds, psychiatric symptomatology and personality traits. Euclidean distance analysis between the personal attributes in the actual selves in the physical and virtual worlds for the similarities of the 2 selves was performed in the analysis. RESULTS: The current primary findings are the negative correlations between the similarity of the personal attributes in the physical actual self and virtual actual self, and smartphone use time, smartphone addiction as well as anxiety symptomatology respectively (P<.05 to P<.01). CONCLUSIONS: The current findings provide empirical evidence for the importance of maintaining a congruent self across the physical and virtual worlds, regulating smartphone use time, preventing smartphone addiction, and safeguarding mental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".