Selfie: A Rising Culture. Assessment of Selfitis and Its Relation with Self-Esteem among Medical and Nursing Students: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Selfitis (the obsessive taking of selfies) is classified as a mental disorder by the American Psychiatric Association and is said to be associated with low self-esteem levels. Assessment of selfitis using Selfitis Behaviour Scale is unique among Indian stud- ies. This study conducted to estimate the burden of selfitis and its relation with self-esteem among medical and nursing students. Methodology: A cross-sectional study was conducted among med- ical and nursing students. Information regarding selfie clicking and posting on social media was collected by a structured questionnaire. Selfitis was assessed by using Selfitis Behaviour Scale (SBS) and self- esteem was assessed by using Rosenberg Self Esteem Scale. Results: This study included 347 students (123 males and 224 fe- males). Majority i.e., 225 (64.8%) of the study participants took selfies on any specific occasion, 300 (86.5%) of them took selfies with friends/ family members and 109 (31.4%) of them posted selfies on social medias. According to the scores of SBS, 169 (48.7%) of them had borderline selfitis, 63 (18.2%) of them had acute selfitis and 13 (3.7%) of them had chronic selfitis. Low self-esteem was seen in 105 (30.3%) of the study participants. Conclusion: Selfitis was seen in nearly quarter of the study partici- pants and there was no association between selfitis and self-esteem in this study.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".