The associations of social networking site use and self-reported general health, mental health, and well-being among Canadians
Bibliographic record
Abstract
Objectives To investigate social networking site (SNS) use and frequency, and their potential associations with self-reported general health, mental health, and well-being among the Canadian population using the nationally representative 2013 General Social Survey (GSS). Methods Data were collected via Statistics Canada GSS 2013 (cycle 27). Six separate one-way analysis of covariances (ANCOVAs) were conducted to determine differences in general health, mental health, and well-being for both SNS use and frequency, controlling for age, gender, number of children at home, household location, education, and income. Results SNS users were younger (with nearly 96% being 15–24 years old vs. 27% ≥ 75 years; p < .001), female ( p < .001), have three or fewer children at home ( p < .001), live in urban/Prince Edward Island locations, were at the lower or higher ends of household income ( p < .001), and were less educated ( p < .001). Among all Internet users, better general health ( p = .03) was associated with using SNSs, yet better mental health ( p = .001) and well-being ( p = .001) were associated with not using SNSs. Among SNS account-holders, those who never accessed their accounts had significantly lower general health ( p = .007), mental health ( p < .001), and well-being ( p < .001) compared with those who accessed their accounts, regardless of frequency. Conclusion Differences exist for SNS use and frequency and health outcomes. However, investigations into the possible differences that may exist between individuals who do not have a SNS account and those who do, but do not use it, are needed in the future.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".