Bibliographic record
Abstract
There is some addictive use of the internet which ultimately refers to a disorder. Internet addiction is characterized by excessive or poorly controlled preoccupations, urges or behaviors regarding computer use and internet access that led to impairment or distress. The diagnosis “Internet Gaming Disorder” (IGD) has been included in the fifth edition of Diagnostic and Statistical Manual of Mental Disorders. This proposed condition is limited to gaming and does not include problems with general use of the internet, online gambling, or use of social media or smartphones.Co morbidity found in this addictive behavior like depression and anxiety. Treatment options are limited, including Cognitive behavioral therapy, family therapy, couple therapy, antidepressant, anti-anxiety drugs and naltrexone. The mental health professionals, information technologists, young and students affairs professionals should be alert to this disorder. Internet addiction is a growing concern in today’s digital age. With the widespread availability and use of the internet, many people are finding it difficult to control their usage, leading to negative consequences in their daily lives. Some of the issues and concerns related to internet addiction include: Social isolation, Poor academic or work performance, Physical health problems, Sleep disturbances, Risks of Cyber bullying, financial problems, Behavioral and Relationship issues. It is important to seek help if you or someone you know is struggling with internet addiction. Management options may include psychotherapy, support groups, and behavior modification techniques to help individuals regain control of their internet use and improve their overall well-being and sometimes medication need when intractable behavioral issues persist. Bangladesh J Medicine 2023; Vol. 34, No. 2(1) Supplement: 183-184
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".