Virtual connection, real support? A study of loneliness, time on social media and psychological distress among men
Bibliographic record
Abstract
BACKGROUND: In an age of increasing loneliness and associated poor mental health, research uncovering the extent to which social connection can be achieved digitally is paramount. This is particularly important among men, who experience unique barriers to achieving meaningful social connections due to masculine norms including independence and self-reliance. Loneliness is a known determinant of both psychological distress and greater time on social media, however relationships among these constructs are yet to be studied specifically among men. AIMS: This study aimed to examine a novel mediation model to uncover whether time on social media mediates the association between loneliness and psychological distress, alongside a moderating effect of age. METHOD: = 13.11) took part via an online survey involving measures of study constructs. RESULTS: Results highlighted a novel moderated mediation effect: for younger men only, loneliness predicts psychological distress via time spent on social media. CONCLUSION: Men experiencing loneliness appear to turn to social media in attempt at digital connection, however for younger men in particular, often this fails to ameliorate links between loneliness and psychological distress. Implications for public health messaging, clinical work with men and future interventional studies are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".