The Role of Need Satisfaction and Frustration in Moderating the Relationship Between Social Media Use and Stress Among University Students
Bibliographic record
Abstract
Research on social media has rarely considered subjective need experiences and how they are related to stress. I hypothesized that need frustration during social media use would amplify the relationship between time spent on social media and stress, whereas need satisfaction during social media use would diminish the relationship. Students (N = 151; Mage = 19.36, SD = 3.77 years, female = 71.5%) completed two self-report surveys one-week apart. Hayes’ (2021) PROCESS macro was used to test moderation. Bivariate correlations revealed that need frustration during social media use was negatively related to stress (rTime1 = .36, rTime2 = .24) and time spent on social media (rTime1 = .30). Need satisfaction was negatively related to stress (rTime1 = -.33, rTime2 = -.24). No significant moderation results were found for need satisfaction (p = .21) and frustration (p = .08). Further research is needed to clarify potential methodological issues that may have contributed to null results.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".