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Record W6962577104 · doi:10.17605/osf.io/e7byx

Loneliness and Sexual Risk in COVID Among Urban GBM in Canada

2022· other· en· W6962577104 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessMental healthSocial isolationSocial supportAnxietyCoping (psychology)Context (archaeology)Social distance

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0050.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.016
GPT teacher head0.295
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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