Prevalence and Effect of Social Media on Sleep among Students of Higher Institutions in Sokoto Metropolis, Sokoto State Nigeria
Bibliographic record
Abstract
Background: Social media use is a very popular leisure activity that is common amongst young people globally. There is increasing evidence that students spend several hours daily on social media with affectation of sleep duration. Sleep duration of 7 to 9 hours daily is needed for normal healthy function of the human body. This study was conducted to determine the effect of social media on sleep pattern among students of higher institutions in Sokoto metropolis, Sokoto State. Methods: This was a cross sectional descriptive study carried out among 381 students of higher institutions in Sokoto metropolis using multistage sampling technique. Frequencies of the various variables were tabulated and chi-square tests were done as appropriate. Results: About 67.4% of the respondents were within the age group 20-24 years. A total of 97.9% of the respondents used social media with WhatsApp ((84.9%) and Facebook ((81.7%) being the commonly used platforms. More than a quarter ((27.4%) used social media for more than 3 hours in a day. About 92% of the respondents used social media at night. Almost half of the respondents who used social media at night have their sleep duration affected by its use. Those using it for more than 2 hours at night were 5 times more likely to have sleep affectation ((AOR=2.9 – 7.3, p<0.000). Conclusion: The study demonstrated high prevalence of social media use amongst students of higher institutions in Sokoto with a significant proportion of them having sleep duration shortened. Schools and the general public should be made to create awareness on the negative impact of using social media among students especially at night.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| 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 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".