Loneliness and Sexual Risk in COVID Among Urban GBM in Canada
Bibliographic record
Abstract
During the peak of the COVID-19 pandemic (thus far) Canadians were ordered to engage in social distancing and social isolation measures. Given that loneliness and perceiving low social support have both been associated with poor mental health outcomes and increased sexual risk-taking behaviour, we wondered how the COVID-19 pandemic would impact the mental health and sexual behaviour of Canadian GBM. In the context of the COVID-19 pandemic, existing work on mixed populations has noted a link between increased perceived social support and decreased experiences of loneliness (e.g., Baraket-Bojmel et al, 2021; Groake et al., 2020) and emotional distress, such as depression (e.g., Grey et al., 2020) and anxiety or stress (e.g., Szkody et al., 2021; Xu et al., 2020). Social support was also found to moderate the association between loneliness and chronic anxiety (Xu et al., 2020). However, some work noted an age-dependent association; Lisitsa et al. (2020) found that younger individuals reported more loneliness and lower social support seeking behaviour. Similarly, Groarke et al. (2020) also found higher rates of loneliness among younger people. The loneliness and sexual risk model posits that sexual risk taking (and substance use) can be the result of maladaptive coping strategies for loneliness, thus younger GBM who experienced more loneliness and less social support in the context of the COVID-19 pandemic may also report poorer mental health and may have been more likely to engage in sexual risk taking behaviours.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".