Engagement in Academic-Based Comparisons on Social Networking Sites and Mental Health Outcomes among Post-secondary Students in Canada: A Cross-sectional Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Post-secondary students are the largest age demographic using social networking sites (SNS). Since students are placed in competitive environments surrounded by peers with similar goals, they are more prone to engaging in upward academic-based comparisons. Given that post-secondary students are already at increased risk of developing mental disorders, determining the link between engagement in online comparisons and mental health outcomes among this population is warranted. OBJECTIVE This paper investigates the relationship between engagement in upward academic-based comparisons on SNS and mental health outcomes among a sample of Canadian post-secondary students. METHODS A secondary analysis of data collected through a national cross-sectional study of post-secondary student mental health and wellbeing was conducted to assess this relationship, controlling for the effects of demographics, SNS platform used, and other comparison-based stressors. RESULTS Four main findings are reported: (1) Associations between engaging in upward academic-based comparisons on SNS and other comparison-based stressors were all statistically significant (P<.001) and students who engaged in upward academic-based comparisons on SNS rated other comparison-based stressors as significantly higher in both severity and frequency compared to those who did not, (2) the association between engaging in upward academic-based comparisons on SNS and the type of SNS platform used was statistically significant across all platforms except Facebook (X2=4.84, P=0.30) and Twitter (X2=4.61, P=0.33), (3) the use of TikTok was linked to worsened mental health outcomes (psychological distress ß = 0.50, P=.26; anxiety ß = 0.32, P=.03; depression ß = 0.45, P=.01) whereas the use of LinkedIn was associated with improved mental health outcomes (psychological distress ß = -1.11, P=.003; anxiety ß = -0.39, P=.05; depression ß = -0.48, P= .04), (4) engagement in upward academic-based comparisons on SNS was significantly related to increased anxiety scores, but not depression or general psychological distress scores. CONCLUSIONS Since the study sample consisted of students attending a single university in Eastern Ontario, these findings are not generalizable to the broader Canadian post-secondary student population. However, they provide evidence that helps contribute to further exploration of this topic area. Future research should explore the effects of online academic-based comparisons on the mental health of post-secondary students in more diverse samples as well as interventions that may help address the mental health impacts of students’ SNS usage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".