Effects of Mobile Phone Withdrawal, Gender and Academic Level on Mobile Phone Dependency Among Mass Communication Students in Ajayi Crowther University, Oyo, Oyo State, Nigeria
Bibliographic record
Abstract
Noticeable among young adults in Nigeria is their dependence on mobile phone for relational communication. This study is therefore one attempt at subjecting such observation to empirical testing. The study examined the effects of Mobile phone withdrawal, gender and academic level of students’ dependency on mobile phone. It was a quasi-experiment with 2×2×>2 non-randomized pre-test post-test control group designs. Subjects in experimental and control groups were 100 and 400 level Mass Communication students of Ajayi Crowther University in Oyo, Oyo State, Nigeria. Students’ Mobile Phone Dependency Questionnaire(r=0.72) was administered as pre and post-test measures. Data generated were analysed with frequency count, percentage, t-test and Analysis of Covariance. Findings show that 55% of the students used their phones ‘very frequently’, 30% used it ‘frequently’. Before the intervention, students’ dependency on mobile phone was ‘moderate’ (60.8%) but after the treatment, there was upward adjustment ‘High’ (45%). Statistical significant difference was found between students’ dependency pre-test and post-test scores in favour of the post-test (t= -5.665; p<0.05). Treatment (F= 3.832; p<0.05) and academic level (F= 12.185; p<0.05) were found to have significant main effects. Hence, the study concluded that students are actually dependent on their mobile phones and that, in considering and controlling mobile phone dependency, students’ academic level is a potent factor.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".