Determinants of computer use: Insights from standard measurements and personal user parameters
Bibliographic record
Abstract
In today’s world majority of the people are using computer and it has made our life very much easier. The present study is conducted in Dr. Rajendra Prasad Central Agricultural University, Kendriya Vidyalaya, Uma Pandey College and Block Office of Pusa Block, Samastipur, Bihar as there were a lot of active computer users. It is estimated that at least 75 per cent of the works are done through the use of computer, but the people are unaware about the health consequences of the computer use. Health problems of the computer users are mainly associated with the duration of computer use, improper computer workplace and the various environmental factors like positioning of computer screen, keyboard and mouse. Results from the study showed that half of the respondents were male and another half were female, among them majority of the users (48.33%) belonged to age group 20-30 years and (43.33%) belonged to age group 30-40 years. Majority of the computer users were working at average level of nature of work and 23.33 per cent of them had worked in poor level. Variables like age, income, gender, education and work environment of the computer users were found to have significant relation with the difficulty in eyes of the respondents and work environment of the users was inversely proportional to the difficulty experiences by the user in eyes. It was concluded that the work of the computer users was sedentary type which gradually develops chronic illness because of the bad postures adopted by the computer users, while performing their work for a prolonged time on a regular basis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".