Increases in Serious Psychological Distress among Ontario Students between 2013 and 2017: Assessing the Impact of Time Spent on Social Media
Bibliographic record
Abstract
OBJECTIVE: The objective of the current research was to examine the association between time spent on social media and serious psychological distress between 2013 and 2017, a period when the rates of both were trending upward. METHODS: = 15,398). Multivariate logistic regression models were used to examine the association between time spent on social media and serious psychological distress controlling for theoretically relevant covariates. Interactions were tested to assess whether the association changed over time. RESULTS: The prevalence of serious psychological distress increased from 10.9% in 2013 to 16.8% in 2017 concomitantly with substantial increases in social media usage, especially at the highest levels. In the multivariate context, we found a significant interaction between social media use and the survey year which indicates that the association between time spent on social media and psychological distress has decreased from 2013 to 2017. CONCLUSION: Although both social media use and psychological distress increased between 2013 and 2017, the interaction between these variables indicates that the strength of this association has decreased over time. This finding suggests that the higher rate of heavy social media use in 2017 compared to 2013 is not actually associated with the higher rate of serious psychological distress during the same time period. From a diffusion of innovation perspective, it is possible that more recent adopters of social media may be less prone to psychological distress. More research is needed to understand the complex and evolving association between social media use and psychological distress. Researchers attempting to isolate the factors associated with the recent increases in psychological distress could benefit from broadening their investigation to factors beyond time spent on social media.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".