The interplay of social media use, social support, and self-regulation in adjusting to university
Bibliographic record
Abstract
The current research examined the characteristics of post-secondary students who use social media, their motivation for using social media and its relationship to university adjustment, as well as the moderating role played by self-regulation, socio-demographic variables, and social support. A total of 403 undergraduate students from two Canadian universities participated in this research, answering questions pertaining to motivations for social media use, social and academic adjustment to university, self-regulation, social support, and socio-demographics. Results show that four motivations for social media use emerged: self-promotion, entertainment, socialization, and university-related. Motivations for social media use were similar across platforms, regardless of socio-demographics, social support, and self-regulation, highlighting the universality of social media. Further findings indicate that the relationship between self-promotion motivations for social media use and social adjustment was moderated by social support, self-regulation, academic performance, and age; whereas there was not relationship between self-promotion and academic adjustment to university. Socialization motivations for social media use was positively linked to social adjustment to university. Self-regulation moderated the relationship between academic adjustment and both socialization and university related motivations for social media use. Lastly, there were no significant relationships or interactions between entertainment motivations for social media sue and either social or academic adjustment to university. As we move into an increasingly technological world, it is important to understand the nuances of how and why emerging adults are using social media to ensure adaptive patterns of internet use, including successful adjustment to university. This work further points to the need to better understand motivations for social media use, in particular, self-promotion motivations, and their impact on the university experience.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".