Digital Platforms as Equivocal Health Promotion: Examining the Mental Health of 2SLGBTQ+ People Related to the COVID-19 Pandemic
Bibliographic record
Abstract
2SLGBTQ+ people often depend on digital platforms, including social media, to connect with members of their community and curate support networks, especially during the COVID-19 pandemic, which has had devastating mental health impacts on marginalized populations. Unfortunately, these same platforms are often engulfed with homophobia and transphobic rhetoric and high rates of online violence targeted at 2SLGBTQ+ communities. To shed light on how digital platforms can help and/or hinder 2SLGBTQ+ people's mental health, we conducted a mixed-methods survey to examine mental health outcomes among 2SLGBTQ+ people in Nova Scotia, Canada, who have used digital platforms during and since the COVID-19 pandemic. The Health Equity Promotion Model was applied to analyze survey data collected from 119 participants and uncover how intersecting individual, structural, and technological factors and affordances impact 2SLGBTQ+ people's mental health. Our findings reflect the diversity and fluidity of 2SLGBTQ+ people's mental health experiences that cannot be viewed through a positivistic lens. We discuss the ethical implications of digital platforms and their mental health effects on marginalized populations and the importance of conducting intersectional research, and we conclude with recommendations to support 2SLGBTQ+ people's mental health.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".